RESEARCH CENTRE

Inria Centre
at Université Côte d’Azur

IN PARTNERSHIP WITH:

CNRS, Université Côte d’Azur

2023
ACTIVITY REPORT

Project-Team

WIMMICS

Web-Instrumented Man-Machine
Interactions, Communities and Semantics

IN COLLABORATION WITH: Laboratoire informatique, signaux systèmes
de Sophia Antipolis (I3S)

DOMAIN

Perception, Cognition and Interaction

THEME

Data and Knowledge Representation and
Processing

Contents

Project-Team WIMMICS

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 Context and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Research Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Research program

3.1 Users Modeling and Designing Interaction on the Web and with AI systems
. . . . . . . . .
3.2 Communities and Social Media Interactions and Content Analysis on the Web and Linked
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Vocabularies, Semantic Web and Linked Data Based Knowledge Representation and Extrac-
tion of Knowledge Graphs on the Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Artificial Intelligence Processing: Learning, Analyzing and Reasoning on Heterogeneous
Knowledge Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

4

6
6
7

8
8

8

8

9

4 Application domains

9
9
4.1 Social Semantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Linked Data on the Web and on Intranets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
4.3 Assisting Web-based Epistemic Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.4 Linked Data for a Web of Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.5 Artificial Web Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.6 Human-Data Interaction (HDI) on the Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.7 Web-augmented interactions with the world . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.8 Analysis of scientific co-authorship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

5 Highlights of the year

12
5.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

6 New software, platforms, open data

13
6.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
6.1.1 ACTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
6.1.2 ARViz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
6.1.3 Attune . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6.1.4 CORESE-Core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6.1.5 CORESE-GUI
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6.1.6 CORESE-Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
6.1.7 CORESE-Command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
6.1.8 CREEP semantic technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
6.1.9 CROBORA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
6.1.10 DBpedia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
6.1.11 Fuzzy labelling argumentation module . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6.1.12 GUsT-3D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6.1.13 IndeGx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6.1.14 ISSA-pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.1.15 ISSA Visualization Web Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.1.16 KartoGraphI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.1.17 Licentia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.1.18 SPARQL Micro-services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
6.1.19 Metadatamatic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
6.1.20 MGExplorer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
6.1.21 Morph-xR2RML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
6.1.22 Muvin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

6.1.23 wam-studio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
6.1.24 WebAudio tube guitar amp sims CLEAN, DISTO and METAL MACHINEs . . . . . . . 22
6.2 Open data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.2.1 CyberAgressionAdo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.2.2 ElecDeb60to20 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6.2.3 ISSA Agritrop Dataset
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6.2.4 WeKG-MF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6.2.5 WheatGenomicsSLKG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
6.2.6 Pharmacogenomics datasets for Ontology Matching . . . . . . . . . . . . . . . . . . . 24
6.2.7 DBpedia.fr : French chapter of the DBpedia knowledge graph dataset . . . . . . . . . 24

7 New results

25
7.1 User Modeling and Designing Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
7.1.1 Spatio-Temporal Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
7.1.2 Tracking and Exploring Audiovisual Archives about the European Union . . . . . . . 25
7.1.3 Interaction with extended reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
7.1.4 Incremental visual exploration of linked data . . . . . . . . . . . . . . . . . . . . . . . 26
7.1.5 Development of an extensible User Profile model . . . . . . . . . . . . . . . . . . . . . 27
7.1.6 Interactive WebAudio applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
7.1.7 KartoGraphI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
7.2 Communities and Social Interactions Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
7.2.1 Autonomous agents in a social and ubiquitous Web . . . . . . . . . . . . . . . . . . . 28
7.2.2 Online Hate Detection in Conversational Data . . . . . . . . . . . . . . . . . . . . . . . 28
7.2.3 Hate Speech Target Community Detection . . . . . . . . . . . . . . . . . . . . . . . . . 29
7.2.4 An In-depth Analysis of Implicit and Subtle Hate Speech Messages
. . . . . . . . . . 29
7.2.5 Generating Adversarial Examples for Implicit Hate Speech Detection . . . . . . . . . 30
7.2.6 Bridging Implicit and Explicit Hate Speech Embedding Representations . . . . . . . 30
7.2.7 Fallacious Argument Classification in Political Debates
. . . . . . . . . . . . . . . . . 30
7.2.8 DISPUTool 2.0: A Modular Architecture for Multi-Layer Argumentative Analysis of

Political Debates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
7.2.9 Argument-based Detection and Classification of Fallacies in Political Debates . . . . 31
7.2.10 Augmenting participation, co-creation, trust and transparency in Deliberative Demo-

cracy at all scales through Argument Mining . . . . . . . . . . . . . . . . . . . . . . . . 31
7.2.11 Unveiling fake news through argumentative evidence . . . . . . . . . . . . . . . . . . 32

7.3 Vocabularies, Semantic Web and Linked Data Based Knowledge Representation and Artifi-

cial Intelligence Formalisms on the Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
7.3.1 Semantic Web for Biodiversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
7.3.2 Semantic Web for Life Sciences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
7.3.3 Evolutionary agent-based evaluation of the sustainability of different knowledge

sharing strategies in open multi-agent systems . . . . . . . . . . . . . . . . . . . . . . 33
7.3.4 W3C Data activity and AC Rep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
7.3.5 AutomaZoo: Automatic annotation of an ancient Zoological Corpus . . . . . . . . . . 34
7.3.6 Contributions to Agronomical knowledge graphs . . . . . . . . . . . . . . . . . . . . . 34
7.3.7 WheatGenomicsSLKG: a Knowledge graph for wheat genomics studies . . . . . . . . 35
7.3.8 Ontology engineering: tooling and methodology . . . . . . . . . . . . . . . . . . . . . 35
7.4 Analyzing and Reasoning on Heterogeneous Semantic Graphs . . . . . . . . . . . . . . . . . 36
7.4.1 Corese Semantic Web Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
7.4.2 SHACL Extension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
7.4.3 Automatic Assessment of Text Difficulty . . . . . . . . . . . . . . . . . . . . . . . . . . 37
7.4.4 On the Automatic Assessment of Natural Language Expert Explanations in Medicine 37
7.4.5 Explanatory Argumentation in Natural Language for Correct and Incorrect Medical

Diagnoses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
7.4.6 ACTA Module Upgrade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
7.4.7 Qualitative evaluation of arguments in persuasive essais . . . . . . . . . . . . . . . . . 38
7.4.8 RDF Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

7.4.9 Capturing Geospatial Knowledge from Real-Estate Advertisements . . . . . . . . . . 39
7.4.10 ISSA: semantic indexing of scientific articles and advanced services . . . . . . . . . . 39
7.4.11 IndeGx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
7.4.12 Metadatamatic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
7.4.13 Learning Pattern-Based Extractors from Natural Language and Knowledge Graphs:

Applying Large Language Models to Wikipedia and Linked Open Data . . . . . . . . 40
. . . . . . . . . 41

7.4.14 AI agent to convert natural language questions into SPARQL queries
7.4.15 Hybridizing machine learning and knowledge graphs: vision, model evaluation, and

knowledge injection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
7.4.16 Semantic Overlay Network for Linked Data Access . . . . . . . . . . . . . . . . . . . . 42

8 Bilateral contracts and grants with industry

42
8.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

9 Partnerships and cooperations

44
9.1 International initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
9.1.1 Associate Teams in the framework of an Inria International Lab or in the framework

9.2 International research visitors

of an Inria International Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
9.1.2 Participation in other International Programs . . . . . . . . . . . . . . . . . . . . . . . 45
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
9.2.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
9.2.2 Visits to international teams
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
9.3.1 H2020 projects
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
9.3.2 Other european programs/initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
9.4 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
9.5 Regional initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

9.3 European initiatives

10 Dissemination

51
10.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
10.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
10.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
10.1.3 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
10.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
10.1.5 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 54
10.1.6 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
10.1.7 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
10.1.8 Promoting Open Science practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
10.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
10.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
10.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
10.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
10.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
10.3.1 Articles and contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
10.3.2 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
10.3.3 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

11 Scientific production

60
11.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
11.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
11.3 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
11.4 Cited publications

Project WIMMICS

1

Project-Team WIMMICS

Creation of the Project-Team: 2013 July 01

Keywords

Computer sciences and digital sciences

A1.2.9. – Social Networks

A1.3.1. – Web

A3.1.1. – Modeling, representation

A3.1.2. – Data management, quering and storage

A3.1.3. – Distributed data

A3.1.4. – Uncertain data

A3.1.6. – Query optimization

A3.1.7. – Open data

A3.1.10. – Heterogeneous data

A3.1.11. – Structured data

A3.2. – Knowledge

A3.2.1. – Knowledge bases

A3.2.2. – Knowledge extraction, cleaning

A3.2.3. – Inference

A3.2.4. – Semantic Web

A3.2.5. – Ontologies

A3.2.6. – Linked data

A3.3.2. – Data mining

A3.4. – Machine learning and statistics

A3.4.1. – Supervised learning

A3.4.6. – Neural networks

A3.4.8. – Deep learning

A3.5. – Social networks

A3.5.1. – Analysis of large graphs

A3.5.2. – Recommendation systems

A5.1. – Human-Computer Interaction

A5.1.1. – Engineering of interactive systems

A5.1.2. – Evaluation of interactive systems

A5.1.9. – User and perceptual studies

A5.2. – Data visualization

A5.7.2. – Music

A5.8. – Natural language processing

2

Inria Annual Report 2023

A7.1.3. – Graph algorithms

A7.2.2. – Automated Theorem Proving

A8.2.2. – Evolutionary algorithms

A9.1. – Knowledge

A9.2. – Machine learning

A9.4. – Natural language processing

A9.6. – Decision support

A9.7. – AI algorithmics

A9.8. – Reasoning

A9.9. – Distributed AI, Multi-agent

A9.10. – Hybrid approaches for AI

Other research topics and application domains

B1.2.2. – Cognitive science

B2. – Health

B5.1. – Factory of the future

B5.6. – Robotic systems

B5.8. – Learning and training

B6.3.1. – Web

B6.3.2. – Network protocols

B6.3.4. – Social Networks

B6.4. – Internet of things

B6.5. – Information systems

B8.5. – Smart society

B8.5.1. – Participative democracy

B9. – Society and Knowledge

B9.1. – Education

B9.1.1. – E-learning, MOOC

B9.1.2. – Serious games

B9.2. – Art

B9.2.1. – Music, sound

B9.3. – Medias

B9.5.1. – Computer science

B9.5.6. – Data science

B9.6. – Humanities

B9.6.1. – Psychology

B9.6.2. – Juridical science

B9.6.5. – Sociology

B9.6.7. – Geography

B9.6.8. – Linguistics

Project WIMMICS

3

B9.6.9. – Political sciences

B9.6.10. – Digital humanities

B9.7. – Knowledge dissemination

B9.7.1. – Open access

B9.7.2. – Open data

B9.9. – Ethics

B9.10. – Privacy

4

Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Fabien Gandon [Team leader, INRIA, Senior Researcher, HDR]

• Victor David [INRIA, ISFP, from Oct 2023]

• Serena Villata [CNRS, Senior Researcher, HDR]

Faculty Members

• Marco Alba Winckler [UNIV COTE D’AZUR, Professor, HDR]

• Michel Buffa [UNIV COTE D’AZUR, Professor, HDR]

• Elena Cabrio [UNIV COTE D’AZUR, Professor, HDR]

• Pierre-Antoine Champin [UNIV LYON, Associate Professor, Delegation, HDR]

• Catherine Faron [UNIV COTE D’AZUR, Professor, HDR]

• Aline Menin [UNIV COTE D’AZUR, Associate Professor]

• Pierre Monnin [UNIV COTE D’AZUR, Associate Professor, from Sep 2023, EFELIA AI Fellow]

• Amaya Nogales Gomez [UNIV COTE D’AZUR, Associate Professor, until Jul 2023, 3AI Fellow]

• Anaïs Ollagnier [UNIV COTE D’AZUR, Associate Professor, from Feb 2023, EFELIA AI Fellow]

• Andrea Tettamanzi [UNIV COTE D’AZUR, Professor, HDR]

Post-Doctoral Fellows

• Ankica Barisic [UNIV COTE D’AZUR, until Oct 2023]

• Cristian Cardellino [UNIV COTE D’AZUR, from Sep 2023]

• Nadia Yacoubi Ayadi [CNRS, until Aug 2023]

PhD Students

• Ali Ballout [UNIV COTE D’AZUR]

• Lucie Cadorel [KINAXIA, CIFRE, (now SEPTEO)]

• Dupuy-Rony Charles [DORIANE, CIFRE]

• Remi Felin [UNIV COTE D’AZUR]

• Pierpaolo Goffredo [CNRS]

• Mina Ilhan [UNIV COTE D’AZUR, GREDEG]

• Santiago Marro [CNRS, until Nov 2023]

• Benjamin Molinet [UNIV COTE D’AZUR]

• Nicolas Ocampo [UNIV COTE D’AZUR]

• Clement Quere [UNIV COTE D’AZUR]

• Shihong Ren [UNIV JEAN MONNET]

Project WIMMICS

5

• Celian Ringwald [INRIA]

• Ekaterina Sviridova [UNIV COTE D’AZUR, from Aug 2023]

• Maroua Tikat [UNIV COTE D’AZUR]

• Xiaoou Wang [CNRS]

Technical Staff

• Arnaud Barbe [INRIA, Engineer, from Aug 2023]

• Anna Bobasheva [INRIA, Engineer, from Dec 2023]

• Remi Ceres [INRIA, Engineer]

• Mariana Eugenia Chaves Espinoza [UNIV COTE D’AZUR, Engineer, from Apr 2023]

• Theo Alkibiades Collias [UNIV COTE D’AZUR, Engineer, from Feb 2023]

• Antoine De Smidt [CNRS, Engineer]

• Molka Dhouib [INRIA, Engineer, from Aug 2023]

• Maxime Lecoq [INRIA, Engineer]

• Christopher Leturc [INRIA, Engineer, until Aug 2023]

• Pierre Maillot [INRIA, Engineer]

• Franck Michel [CNRS, Engineer]

• Iliana Petrova [INRIA, Engineer, until Jun 2023]

• Nicolas Robert [INRIA, Engineer, from Sep 2023]

• Ekaterina Sviridova [UNIV COTE D’AZUR, Engineer, until Aug 2023]

Interns and Apprentices

• Nicolas Audoux [INRIA, Intern, from Jun 2023 until Sep 2023]

• Hugo Carton [INRIA, Apprentice, from Nov 2023]

• Deborah Dore [UNIV SAPIENZA , Intern, from Sep 2023]

• Erwan Hain [UNIV COTE D’AZUR, Intern, from Apr 2023 until Jun 2023]

• Raphael Julien [UNIV COTE D’AZUR, Intern, from Mar 2023 until Aug 2023]

• Ekaterina Kolos [CNRS, Intern]

• Thomas Mac Vicar [YNOV CAMPUS, Intern, from May 2023 until Aug 2023]

• Quentin Plet [INRIA, Intern, from Mar 2023 until Aug 2023]

• Rim Richa [UNIV COTE D’AZUR, Intern, until Jun 2023]

• Martina Rossini [CNRS„ Intern, until Feb 2023]

• Antoine Vidal Mazuy [INRIA, Apprentice, until Aug 2023]

• Manuel Vimercati [UNIV MILANO, Intern]

6

Inria Annual Report 2023

Administrative Assistants

• Delphine Robache [INRIA]

• Lionel Tavanti [UNIV COTE D’AZUR, I3S]

Visiting Scientists

• Greta Damo [UNIV COTE D’AZUR, from Sep 2023]

• Roberto Demaria [UNI TORINO, from Jul 2023 until Sep 2023]

• Cristhian Figueroa [UNIV DEL CAUCA, from Jun 2023 until Jul 2023, Assistant Professor]

• Yusuke Higuchi [JPO (Japan Patent Office), until Aug 2023]

• Dario Malchiodi [UNIV MILANO, from Oct 2023]

• Smaranda Muresan [Columbia University, from May 2023 until Jun 2023, Research Scientist]

• Matteo Palmonari [UNIV MILANO BICOCCA, from Jun 2023 until Jul 2023, Professor]

External Collaborators

• Hanna Abi Akl [DSTI, from Feb 2023]

• Helena Bonaldi [UNIV TRENTE, from Nov 2023]

• Andrei Ciortea [UNIV ST GALLEN, Assistant Professor]

• Olivier Corby [INRIA, Retired Researcher]

• Alain Giboin [INRIA, Retired Researcher]

• Freddy Lecue [JP MORGAN, AI Research Director]

• Oscar Rodriguez Rocha [TEACH ON MARS]

• Stefan Sarkadi [KINGS COLLEGE LONDON, Research Fellow]

2 Overall objectives

2.1 Context and Objectives

The World Wide Web has transformed into a virtual realm where individuals and software interact in
diverse communities. The Web has the potential to become the collaborative space for both natural and
artificial intelligence, thereby posing the challenge of supporting these global interactions. The large-
scale, mixed interactions inherent in this scenario present a plethora of issues that must be addressed
through multidisciplinary approaches[110].

One particular problem is to reconcile the formal semantics of computer science (such as logics,
ontologies, typing systems, protocols, etc.) on which the Web architecture is built, with the soft semantics
of human interactions (such as posts, tags, status, relationships, etc.) that form the foundation of Web
content. This requires a holistic approach that considers both the technical and social aspects of the Web,
in order to ensure that the interactions between computational and natural intelligence are seamless and
meaningful.

Wimmics proposes a range of models and methods to bridge the gap between formal semantics and
social semantics on the World Wide Web[111], in order to address some of the challenges associated with
constructing a universal space that connects various forms of intelligence.

From a formal modeling point of view, one of the consequences of the evolutions of the Web is that
the initial graph of linked pages has been joined by a growing number of other graphs. This initial graph

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is now mixed with sociograms capturing the social network structure, workflows specifying the decision
paths to be followed, browsing logs capturing the trails of our navigation, service compositions specifying
distributed processing, open data linking distant datasets, etc. Moreover, these graphs are not available
in a single central repository but distributed over many different sources. Some sub-graphs are small
and local (e.g. a user’s profile on a device), some are huge and hosted on clusters (e.g. Wikipedia), some
are largely stable (e.g. thesaurus of Latin), some change several times per second (e.g. social network
statuses), etc. Moreover, each type of network of the Web is not an isolated island. Networks interact
with each other: the networks of communities influence the message flows, their subjects and types, the
semantic links between terms interact with the links between sites and vice-versa, etc.

Not only do we need means to represent and analyze each kind of graphs, we also do need means to
combine them and to perform multi-criteria analysis on their combination. Wimmics contributes to these
challenges by: (1) proposing multidisciplinary approaches to analyze and model the many aspects of
these intertwined information systems, their communities of users and their interactions; (2) formalizing
and reasoning on these models using graphs-based knowledge representation from the semantic Web [1]
to propose new analysis tools and indicators, and to support new functionalities and better management.
In a nutshell, the first research direction looks at models of systems, users, communities and interactions
while the second research direction considers formalisms and algorithms to represent them and reason
on their representations.

2.2 Research Topics

The research objectives of Wimmics can be grouped according to four topics that we identify in reconciling
social and formal semantics on the Web:

Topic 1 - users modeling and designing interaction on the Web and with knowledge graphs: The
general research question addressed by this objective is “How do we improve our interactions with a
semantic and social Web more and more complex and dense ?”. Wimmics focuses on specific sub-questions:
“How can we capture and model the users’ characteristics?” “How can we represent and reason with
the users’ profiles?” “How can we adapt the system behaviors as a result?” “How can we design new
interaction means?” “How can we evaluate the quality of the interaction designed?”. This topic includes a
long-term research direction in Wimmics on information visualization of semantic graphs on the Web.
The general research question addressed in this last objective is “How to represent the inner and complex
relationships between data obtained from large and multivariate knowledge graph?”. Wimmics focuses on
several sub-questions: ”Which visualization techniques are suitable (from a user point of view) to support
the exploration and the analysis of large graphs?” How to identify the new knowledge created by users
during the exploration of knowledge graph ?” “How to formally describe the dynamic transformations
allowing to convert raw data extracted from the Web into meaningul visual representations?” “How
to guide the analysis of graphs that might contain data with diverse levels of accuracy, precision and
interestingness to the users?”

Topic 2 - communities and social interactions and content analysis on the Web: The general
question addressed in this second objective is “How can we manage the collective activity on social media?”.
Wimmics focuses on the following sub-questions: “How do we analyze the social interaction practices
and the structures in which these practices take place?” “How do we capture the social interactions and
structures?” “How can we formalize the models of these social constructs?” “How can we analyze and
reason on these models of the social activity ?”

Topic 3 - vocabularies, semantic Web and linked data based knowledge extraction and represent-
ation with knowledge graphs on the Web: The general question addressed in this third objective is
“What are the needed schemas and extensions of the semantic Web formalisms for our models?”. Wimmics
focuses on several sub-questions: “What kinds of formalism are the best suited for the models of the
previous section?” “What are the limitations and possible extensions of existing formalisms?” “What
are the missing schemas, ontologies, vocabularies?” “What are the links and possible combinations
between existing formalisms?” We also address the question of knowledge extraction and especially AI
and NLP methods to extract knowledge from text. In a nutshell, an important part of this objective is to
formalize the models identified in the previous objectives as typed graphs and to populate thems in order
for software to exploit these knowledge graphs in their processing (in the next objective).

Topic 4 - artificial intelligence processing: learning, analyzing and reasoning on heterogeneous

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Inria Annual Report 2023

semantic graphs on the Web: The general research question addressed in this objective is “What are the
algorithms required to analyze and reason on the heterogeneous graphs we obtained?”. Wimmics focuses
on several sub-questions: ”How do we analyze graphs of different types and their interactions?” “How do
we support different graph life-cycles, calculations and characteristics in a coherent and understandable
way?” “What kind of algorithms can support the different tasks of our users?”.

3 Research program

3.1 Users Modeling and Designing Interaction on the Web and with AI systems

Wimmics focuses on interactions of ordinary users with ontology-based knowledge systems, with a
preference for semantic Web formalisms and Web 2.0 applications. We specialize in interaction design
and evaluation methods to Web application tasks such as searching, browsing, contributing or protecting
data. The team is especially interested in using semantics in assisting the interactions. We propose
knowledge graph representations and algorithms to support interaction adaptation, for instance for
context-awareness or intelligent interactions with machine. We propose and evaluate Web-based visualiz-
ation techniques for linked data, querying, reasoning, explaining and justifying. Wimmics also integrates
natural language processing approaches to support natural language based interactions. We rely on
cognitive studies to build models of the system, the user and the interactions between users through
the system, in order to support and improve these interactions. We extend the user modeling technique
known as Personas where user models are represented as specific, individual humans. Personas are
derived from significant behavior patterns (i.e., sets of behavioral variables) elicited from interviews with
and observations of users (and sometimes customers) of the future product. Our user models specialize
Personas approaches to include aspects appropriate to Web applications. Wimmics also extends user
models to capture very different aspects (e.g. emotional states).

3.2 Communities and Social Media Interactions and Content Analysis on the Web

and Linked Data

The domain of social network analysis is a whole research domain in itself and Wimmics targets what can
be done with typed graphs, knowledge representations and social models. We also focus on the specificity
of social Web and semantic Web applications and in bridging and combining the different social Web data
structures and semantic Web formalisms. Beyond the individual user models, we rely on social studies to
build models of the communities, their vocabularies, activities and protocols in order to identify where
and when formal semantics is useful. We propose models of collectives of users and of their collaborative
functioning extending the collaboration personas and methods to assess the quality of coordination
interactions and the quality of coordination artifacts. We extend and compare community detection
algorithms to identify and label communities of interest with the topics they share. We propose mixed
representations containing social semantic representations (e.g. folksonomies) and formal semantic
representations (e.g. ontologies) and propose operations that allow us to couple them and exchange
knowledge between them. Moving to social interaction we develop models and algorithms to mine and
integrate different yet linked aspects of social media contributions (opinions, arguments and emotions)
relying in particular on natural language processing and argumentation theory. To complement the study
of communities we rely on multi-agent systems to simulate and study social behaviors. Finally we also
rely on Web 2.0 principles to provide and evaluate social Web applications.

3.3 Vocabularies, Semantic Web and Linked Data Based Knowledge Representation

and Extraction of Knowledge Graphs on the Web

For all the models we identified in the previous sections, we rely on and evaluate knowledge representation
methodologies and theories, in particular ontology-based modeling. We also propose models and
formalisms to capture and merge representations of different levels of semantics (e.g. formal ontologies
and social folksonomies). The important point is to allow us to capture those structures precisely and
flexibly and yet create as many links as possible between these different objects. We propose vocabularies

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and semantic Web formalizations for all the aspects that we model and we consider and study extensions
of these formalisms when needed. The results have all in common to pursue the representation and
publication of our models as linked data. We also contribute to the extraction, transformation and
linking of existing resources (informal models, databases, texts, etc.) to publish knowledge graphs
on the Semantic Web and as Linked Data. Examples of aspects we formalize include: user profiles,
social relations, linguistic knowledge, bio-medical data, business processes, derivation rules, temporal
descriptions, explanations, presentation conditions, access rights, uncertainty, emotional states, licenses,
learning resources, etc. At a more conceptual level we also work on modeling the Web architecture with
philosophical tools so as to give a realistic account of identity and reference and to better understand the
whole context of our research and its conceptual cornerstones.

3.4 Artificial Intelligence Processing: Learning, Analyzing and Reasoning on Het-

erogeneous Knowledge Graphs

One of the characteristics of Wimmics is to rely on graph formalisms unified in an abstract graph model
and operators unified in an abstract graph machine to formalize and process semantic Web data, Web
resources, services metadata and social Web data. In particular Corese, the core software of Wimmics,
maintains and implements that abstraction. We propose algorithms to process the mixed representations
of the previous section. In particular we are interested in allowing cross-enrichment between them and
in exploiting the life cycle and specificity of each one to foster the life-cycles of the others. Our results all
have in common to pursue analyzing and reasoning on heterogeneous knowledge graphs issued from
social and semantic Web applications. Many approaches emphasize the logical aspect of the problem
especially because logics are close to computer languages. We defend that the graph nature of Linked
Data on the Web and the large variety of types of links that compose them call for typed graphs models.
We believe the relational dimension is of paramount importance in these representations and we propose
to consider all these representations as fragments of a typed graph formalism directly built above the
Semantic Web formalisms. Our choice of a graph based programming approach for the semantic and
social Web and of a focus on one graph based formalism is also an efficient way to support interoperability,
genericity, uniformity and reuse.

4 Application domains

4.1 Social Semantic Web

A number of evolutions have changed the face of information systems in the past decade but the advent
of the Web is unquestionably a major one and it is here to stay. From an initial wide-spread perception of
a public documentary system, the Web as an object turned into a social virtual space and, as a technology,
grew as an application design paradigm (services, data formats, query languages, scripting, interfaces,
reasoning, etc.). The universal deployment and support of its standards led the Web to take over nearly all
of our information systems. As the Web continues to evolve, our information systems are evolving with it.
Today in organizations, not only almost every internal information system is a Web application,
but these applications more and more often interact with external Web applications. The complexity
and coupling of these Web-based information systems call for specification methods and engineering
tools. From capturing the needs of users to deploying a usable solution, there are many steps involving
computer science specialists and non-specialists.

We defend the idea of relying on Semantic Web formalisms to capture and reason on the models of
these information systems supporting the design, evolution, interoperability and reuse of the models and
their data as well as the workflows and the processing.

4.2 Linked Data on the Web and on Intranets

With billions of triples online (see Linked Open Data initiative), the Semantic Web is providing and linking
open data at a growing pace and publishing and interlinking the semantics of their schemas. Information
systems can now tap into and contribute to this Web of data, pulling and integrating data on demand.

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Inria Annual Report 2023

Many organisations also started to use this approach on their intranets leading to what is called linked
enterprise data.

A first application domain for us is the publication and linking of data and their schemas through
Web architectures. Our results provide software platforms to publish and query data and their schemas,
to enrich these data in particular by reasoning on their schemas, to control their access and licenses, to
assist the workflows that exploit them, to support the use of distributed datasets, to assist the browsing
and visualization of data, etc.

Examples of collaboration and applied projects include: Corese, DBpedia.fr, DekaLog, D2KAB,

MonaLIA.

4.3 Assisting Web-based Epistemic Communities

In parallel with linked open data on the Web, social Web applications also spread virally (e.g. Facebook
growing toward 1.5 billion users) first giving the Web back its status of a social read-write media and
then putting it back on track to its full potential of a virtual place where to act, react and interact. In
addition, many organizations are now considering deploying social Web applications internally to foster
community building, expert cartography, business intelligence, technological watch and knowledge
sharing in general.

By reasoning on the Linked Data and the semantics of the schemas used to represent social structures
and Web resources, we provide applications supporting communities of practice and interest and fos-
tering their interactions in many different contexts (e-learning, business intelligence, technical watch,
etc.).

We use typed graphs to capture and mix: social networks with the kinds of relationships and the
descriptions of the persons; compositions of Web services with types of inputs and outputs; links between
documents with their genre and topics; hierarchies of classes, thesauri, ontologies and folksonomies;
recorded traces and suggested navigation courses; submitted queries and detected frequent patterns;
timelines and workflows; etc.

Our results assist epistemic communities in their daily activities such as biologists exchanging results,
business intelligence and technological watch networks informing companies, engineers interacting on
a project, conference attendees, students following the same course, tourists visiting a region, mobile
experts on the field, etc. Examples of collaboration and applied projects: ISSA, TeachOnMars, CREEP,
ATTENTION, CIGAIA.

4.4 Linked Data for a Web of Diversity

We intend to build on our results on explanations (provenance, traceability, justifications) and to continue
our work on opinions and arguments mining toward the global analysis of controversies and online
debates. One result would be to provide new search results encompassing the diversity of viewpoints
and providing indicators supporting opinion and decision making and ultimately a Web of trust. Trust
indicators may require collaborations with teams specialized in data certification, cryptography, signature,
security services and protocols, etc. This will raise the specific problem of interaction design for security
and privacy. In addition, from the point of view of the content, this requires to foster the publication
and coexistence of heterogeneous data with different points of views and conceptualizations of the
world. We intend to pursue the extension of formalisms to allow different representations of the world to
co-exist and be linked and we will pay special attention to the cultural domain and the digital humanities.
Examples of collaboration and applied projects: ACTA, DISPUTOOL.

4.5 Artificial Web Intelligence

We intend to build on our experience in artificial intelligence (knowledge representation, reasoning)
and distributed artificial intelligence (multi-agent systems - MAS) to enrich formalisms and propose
alternative types of reasoning (graph-based operations, reasoning with uncertainty, inductive reasoning,
non-monotonic, etc.) and alternative architectures for linked data with adequate changes and extensions
required by the open nature of the Web. There is a clear renewed interest in AI for the Web in general and
for Web intelligence in particular. Moreover, distributed AI and MAS provide both new architectures and

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new simulation platforms for the Web. At the macro level, the evolution accelerated with HTML5 toward
Web pages as full applications and direct Page2Page communication between browser clearly is a new
area for MAS and P2P architectures. Interesting scenarios include the support of a strong decentralization
of the Web and its resilience to degraded technical conditions (downscaling the Web), allowing pages
to connect in a decentralized way, forming a neutral space, and possibly going offline and online again
in erratic ways. At the micro level, one can imagine the place RDF (Resource Description Framework)
and SPARQL (SPARQL Protocol and RDF Query Language) could take as data model and programming
model in the virtual machines of these new Web pages and, of course, in the Web servers. RDF is also
used to serialize and encapsulate other languages and becomes a pivot language in linking very different
applications and aspects of applications. Example of collaboration and applied projects: HyperAgents,
DekaLog, AI4EU, AI4Media.

4.6 Human-Data Interaction (HDI) on the Web

We need more interaction design tools and methods for linked data access and contribution. We intend
to extend our work on exploratory search coupling it with visual analytics to assist sense making. It could
be a continuation of the Gephi extension that we built targeting more support for non experts to access
and analyze data on a topic or an issue of their choice. More generally speaking SPARQL is inappropriate
for common users and we need to support a larger variety of interaction means with linked data. We
also believe linked data and natural language processing (NLP) have to be strongly integrated to support
natural language based interactions. Linked Open Data (LOD) for NLP, NLP for LOD and Natural Dialog
Processing for querying, extracting and asserting data on the Web is a priority to democratize its use.
Micro accesses and micro contributions are important to ensure public participation and also call for
customized interfaces and thus for methods and tools to generate these interfaces. In addition, the user
profiles are being enriched now with new data about the user such as his/her current mental and physical
state, the emotion he/she just expressed or his/her cognitive performances. Taking into account this
information to improve the interactions, change the behavior of the system and adapt the interface is
a promising direction. And these human-data interaction means should also be available for “small
data”, helping the user to manage his/her personal information and to link it to public or collective one,
maintaining his/her personal and private perspective as a personal Web of data. Finally, the continuous
knowledge extractions, updates and flows add the additional problem of representing, storing, querying
and interacting with dynamic data. Examples of collaboration and applied projects: WASABI, MuvIn,
LDViz.

4.7 Web-augmented interactions with the world

The Web continues to augment our perception and interaction with reality. In particular, Linked Open
Data enable new augmented reality applications by providing data sources on almost any topic. The
current enthusiasm for the Web of Things, where every object has a corresponding Web resource, requires
evolutions of our vision and use of the Web architecture. This vision requires new techniques as the
ones mentioned above to support local search and contextual access to local resources but also new
methods and tools to design Web-based human devices interactions, accessibility, etc. These new usages
are placing new requirements on the Web Architecture in general and on the semantic Web models
and algorithms in particular to handle new types of linked data. They should support implicit requests
considering the user context as a permanent query. They should also simplify our interactions with
devices around us jointly using our personal preferences and public common knowledge to focus the
interaction on the vital minimum that cannot be derived in another way. For instance, the access to the
Web of data for a robot can completely change the quality of the interactions it can offer. Again, these
interactions and the data they require raise problems of security and privacy. Examples of collaboration
and applied projects: ALOOF, AZKAR, MoreWAIS.

4.8 Analysis of scientific co-authorship

Over the last decades, scientific research has matured and diversified. In all areas of knowledge, we
observe an increasing number of scientific publications, a rapid development of ever more specialized

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Inria Annual Report 2023

conferences and journals, and the creation of dynamic collaborative networks that cross borders and
evolve over time. In this context, analyzing scientific publications and the resulting inner co-authorship
networks is a major issue for the sustainability of scientific research. To illustrate this, let us consider
what happens in the context of the COVID-19 pandemics, when the whole scientific community engaged
numerous fields of research to contribute in a common effort to study, understand and fight the severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In order to support the scientific community,
many datasets covering the publications about coronaviruses and related diseases have been compiled.
In a short time, the number of publications available (over 200,000+ and still increasing) suggests that it is
impossible for any researcher to examine every publication and extract the relevant information.

By reasoning on the Linked Data and Web semantic schemas, we investigate methods and tools to
assist users on finding relevant publications to answer their research questions. Hereafter we present
some example of typical domain questions and how we can contribute to the matter.

• How to find relevant publication in huge datasets? We investigate the use of association rules as a
suitable solution to identify relevant scientific publications. By extracting association rules that
determine the co-occurrence between terms in a text, it is possible to create clusters of scientific
publications that follow a certain pattern; users can focus the search on clusters that contain the
terms of interests rather than search the whole dataset.

• How to explain the contents of scientific publications? By reasoning on the Linked Data and Web
semantic schemas, we investigate methods for the creation and exploration of argument graphs
that describe association and development of ideas in scientific papers.

• How to understand the impact of co-authorship (collaboration of one or more authors) in the
development of scientific knowledge? For that, we proposed visualization techniques that allows
the description of co-authorship networks describing the clusters of collaborations that evolve over
time. Co-authorship networks can inform both collaboration between authors and institutions.

Currently, the analysis of co-publications has been performed over two majors datasets: Hal open archive,
the Covid-on-the-Web datasets, and the Agritrop (CIRAD’s open dataset).

5 Highlights of the year

We have three new young researchers in the team : Victor David (ISFP Inria), Anaïs Ollagnier (EFELIA
Fellow) and Pierre Monnin (EFELIA Fellow)

We have renewed the following 3IA Chairs: Elena Cabrio, Fabien Gandon and Serena Villata.

5.1 Awards

The article "Probabilistic Information in SHACL Validation Reports" [61] by Rémi Felin, Catherine Faron
and Andrea G. B. Tettamanzi receives the exæquo best research paper award at the European Conference
for Semantic Web (ESWC 2023) and the best spotlight paper award at Ingénierie des Connaissances (IC
2023).

The article "An Integrated Framework for Understanding Multimodal Embodied Experiences in
Interactive Virtual Reality" [79] by Florent Robert, Hui-Yin Wu, Lucile Sassatelli, Stephen Ramanoël,
Auriane Gros, and Marco Winckler, receives the best paper award at the ACM International Conference
on Interactive Media Experiences (ACM IMX 2023).

The paper "Driver Model for Take-Over-Request in Autonomous Vehicles" [54] by Ankica Barisic,
Pierre Sigrist, Sylvain Oliver, Aurélien Sciarra, Marco Winckler, receives the best paper award at the
Workshop HAAPIE 2023: Human Aspects in Adaptive and Personalized Interactive Environments, held in
conjunction with the 31st ACM Conference on User Modeling, Adaptation and Personalization, UMAP
2023, Limassol, Cyprus, June 26-29, 2023.

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6 New software, platforms, open data

6.1 New software

6.1.1 ACTA

Name: A Tool for Argumentative Clinical Trial Analysis

Keywords: Artificial intelligence, Natural language processing, Argument mining

Functional Description: Argumentative analysis of textual documents of various nature (e.g., persuasive
essays, online discussion blogs, scientific articles) allows to detect the main argumentative com-
ponents (i.e., premises and claims) present in the text and to predict whether these components
are connected to each other by argumentative relations (e.g., support and attack), leading to the
identification of (possibly complex) argumentative structures. Given the importance of argument-
based decision making in medicine, ACTA is a tool for automating the argumentative analysis of
clinical trials. The tool is designed to support doctors and clinicians in identifying the document(s)
of interest about a certain disease, and in analyzing the main argumentative content and PICO
elements.

URL: http://ns.inria.fr/acta/

Contact: Serena Villata

6.1.2 ARViz

Name: Association Rules Visualization

Keyword: Information visualization

Scientific Description: ARViz supports the exploration of data from named entities knowledge graphs
based on the joint use of association rule mining and visualization techniques. The former is a
widely used data mining method to discover interesting correlations, frequent patterns, associ-
ations, or casual structures among transactions in a variety of contexts. An association rule is an
implication of the form X -> Y, where X is an antecedent itemset and Y is a consequent itemset,
indicating that transactions containing items in set X tend to contain items in set Y. Although the
approach helps reduce and focus the exploration of large datasets, analysts are still confronted
with the inspection of hundreds of rules in order to grasp valuable knowledge. Moreover, when
extracting association rules from named entities (NE) knowledge graphs, the items are NEs that
form antecedent -> consequent links, which the user should be able to cross to recover information.
In this context, information visualization can help analysts visually identify interesting rules that
are worthy of further investigation, while providing suitable visual representation to communicate
the relationships between itemsets and association rules.

Functional Description: ARViz supports the exploration of thematic attributes describing association
rules (e.g. confidence, interestingness, and symmetry) through a set of interactive, synchronized,
and complementary visualisation techniques (i.e. a chord diagram, an association graph, and a
scatter plot). Furthermore, the interface allows the user to recover the scientific publications related
to rules of interest.

Release Contributions: Visualization of association rules within the scientific literature of COVID-19.

URL: http://covid19.i3s.unice.fr:8080/arviz/

Publication: hal-03292140

Contact: Marco Alba Winckler

Participants: Aline Menin, Lucie Cadorel, Andrea Tettamanzi, Alain Giboin, Fabien Gandon, Marco Alba

Winckler

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6.1.3 Attune

Inria Annual Report 2023

Name: Attune - A Web-Based Digital Audio Workstation to Empower Cochlear Implant Users

Keywords: Web Application, Audio signal processing, Plug-in

Functional Description: Attune is online software based on the Wam-Studio open-source digital audio
workstation, adapted to help cochlear implant users perceive music more clearly. During multi-
track listening, simple settings such as "clarity", "power", "attenuation" can be used. In reality,
these settings control many parameters of sound processing plugins, which operate behind the
scenes. The mapping between these plugins and the settings offered to users is carried out by
researchers, using a dedicated graphical interface and a powerful macro management system.

Contact: Michel Buffa

Partner: CCRMA Lab, Stanford

6.1.4 CORESE-Core

Name: COnceptual REsource Search Engine - Core

Keywords: Semantic Web, RDF, RDFS, SPARQL, OWL, SHACL, Automated Reasoning, Validation, Interop-
erability, Linked Data, Knowledge Graphs, Knowledge Bases, Knowledge representation, Querying,
Ontologies

Scientific Description: CORESE-Core is a library used in research to apply and evaluate Semantic Web
standards and the algorithms they require. It is also the basis for proposing and prototyping
extensions to these standards and their processing.

Functional Description: CORESE-Core is a library that implements and extends the Semantic Web
standards established by the W3C, such as RDF, RDFS, SPARQL1.1 Query & Update, OWL RL,
SHACL, and others.

This library offers a wide range of features for creating, manipulating, parsing, serializing, querying,
reasoning and validating RDF data.

In addition, it offers advanced extensions such as STTL, SPARQL Rule and LDScript, which extend
the functionality and processing capabilities of the data.

NB: CORESE-Core is a library derived from the earlier CORESE software.

Release Contributions: https://github.com/Wimmics/corese/blob/master/CHANGELOG.md

URL: http://project.inria.fr/corese

Contact: Remi Ceres

Participants: Remi Ceres, Fabien Gandon

6.1.5 CORESE-GUI

Name: COnceptual REsource Search Engine - Graphical User Interface

Keywords: GUI (Graphical User Interface), User Interfaces, Knowledge Bases, Knowledge Graphs, Know-
ledge graph, Knowledge representation, Ontologies, Linked Data, Validation, Automated Reasoning,
SHACL, OWL, SPARQL, RDFS, RDF, Querying, Applications

Scientific Description: CORESE-GUI is a graphical user interface developed to interact with the CORESE-
Core library. It provides users, especially those less experienced in programming, with an intuitive
and visual access to the functionalities of CORESE-Core. This interface includes tools for visualizing
semantic data, editing SPARQL queries, and monitoring data processing results. CORESE-GUI also
serves as a platform for experimenting with new extensions and processing methods in the field of
semantic web, thereby making these technologies more accessible to researchers and practitioners.

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Functional Description: This desktop application allows the user to call up CORESE-Core features for
creating, manipulating, parsing, serializing, querying, reasoning and validating RDF data.

The application enables direct use of Semantic Web languages standardized by the W3C, such as
RDF and its syntaxes, RDFS, SPARQL1.1 Query & Update, OWL RL, SHACL, and others.

URL: http://project.inria.fr/corese

Contact: Remi Ceres

Participants: Remi Ceres, Fabien Gandon

6.1.6 CORESE-Server

Name: COnceptual REsource Search Engine - Server

Keywords: Server, Linked Data, Semantic Web, Ontologies, Knowledge Graphs, Knowledge Bases, RDF,

RDFS, SPARQL, SHACL, Querying, Validation, Automated Reasoning

Scientific Description: This server version allows remote applications to access CORESE-Core function-
alities for creating, manipulating, analyzing, serializing, querying, reasoning, and validating RDF
data. The server facilitates remote use of W3C-standardized Semantic Web languages, such as RDF
and its syntaxes, RDFS, SPARQL1.1 Query & Update, OWL RL, SHACL, and more.

Functional Description: This server version enables a remote application to call CORESE-Core’s func-
tions for creating, manipulating, analyzing, serializing, querying, reasoning and validating RDF
data.

The server enables remote use of Semantic Web languages standardized by the W3C, such as RDF
and its syntaxes, RDFS, SPARQL1.1 Query & Update, OWL RL, SHACL, and others.

URL: https://project.inria.fr/corese/

Contact: Remi Ceres

Participants: Remi Ceres, Fabien Gandon

6.1.7 CORESE-Command

Name: COnceptual REsource Search Engine - Command Line

Keywords: Command, RDF, RDFS, SPARQL, SHACL, Knowledge acquisition

Scientific Description: This command-line version of CORESE enables users to incorporate CORESE-
Core functionalities into scripts, workflows, and consoles for creating, manipulating, analyzing,
serializing, querying, reasoning, and validating RDF data. It allows direct use of W3C-standardized
Semantic Web languages, such as RDF and its syntaxes, RDFS, SPARQL1.1 Query & Update, OWL
RL, SHACL, and more.

Functional Description: This command-line version enables users to call CORESE-Core’s functionality
in scripts, workflows and console mode for the creation, manipulation, analysis, serialization,
querying, reasoning and validation of RDF data.

The command enables direct use of W3C-standardized Semantic Web languages, such as RDF and
its syntaxes, RDFS, SPARQL1.1 Query & Update, OWL RL, SHACL, and others.

URL: https://project.inria.fr/corese/

Contact: Remi Ceres

Participants: Remi Ceres, Fabien Gandon

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Inria Annual Report 2023

6.1.8 CREEP semantic technology

Keywords: Natural language processing, Machine learning, Artificial intelligence

Scientific Description: The software provides a modular architecture specifically tailored at the classi-
fication of cyberbullying and offensive content on social media platforms. The system can use
a variety of features (ngrams, different word embeddings, etc) and all the netwok parameters
(number of hidden layers, dropout, etc) can be altered by using a configuration file.

Functional Description: The software uses machine learning techniques to classify cyberbullying in-

stances in social media interactions.

Release Contributions: Attention mechanism, Hyperparameters for emoji in config file, Predictions

output, Streamlined labeling of arbitrary files

Publications: hal-01906096v1, hal-01920266v1

Contact: Michele Corazza

Participants: Michele Corazza, Elena Cabrio, Serena Villata

6.1.9 CROBORA

Name: Crossing borders Archives. The circulation of images of Europe.

Keywords: Audiovisual, Data visualization

Functional Description: This platform gives access to 36 000 stock shots reused in the evening news of
six national channels in France and Italy (France 2, Arte, TF1, Rai Uno, Rai Due and Canale 5) and
the YouTube accounts of the European institutions between 2001 and 2021. The platform gives
access four types of data: screenshots (one for each stock shot), metadata (for each stock shot),
videos (news), and original metadata for each video. The platform integrates three visualization
tools (Treemaps, Muvin, and ArViz) presenting patterns and relationships between records. The
tool is available at https://crobora.huma-num.fr/crobora?tab=0

Contact: Marco Alba Winckler

6.1.10 DBpedia

Name: DBpedia

Keywords: RDF, SPARQL

Functional Description: DBpedia is an international crowd-sourced community effort to extract struc-
tured information from Wikipedia and make this information available on the semantic Web as
linked open data. The DBpedia triple stores then allow anyone to solve sophisticated queries
against Wikipedia extracted data, and to link the different data sets on these data. The French
chapter of DBpedia was created and deployed by Wimmics and is now an online running platform
providing data to several projects such as: QAKIS, Izipedia, zone47, Sépage, HdA Lab., JocondeLab,
etc.

Release Contributions: The new release is based on updated Wikipedia dumps and the inclusion of the

DBpedia history extraction of the pages.

URL: http://wiki.dbpedia.org/

Contact: Fabien Gandon

Participants: Fabien Gandon, Elmahdi Korfed

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6.1.11 Fuzzy labelling argumentation module

Name: Fuzzy labelling algorithm for abstract argumentation

Keywords: Artificial intelligence, Multi-agent, Knowledge representation, Algorithm

Functional Description: The goal of the algorithm is to compute the fuzzy acceptability degree of a set
of arguments in an abstract argumentation framework. The acceptability degree is computed from
the trustworthiness associated with the sources of the arguments.

Contact: Serena Villata

Participant: Serena Villata

6.1.12 GUsT-3D

Name: Guided User Tasks Unity plugin for 3D virtual reality environments

Keywords: 3D, Virtual reality, Interactive Scenarios, Ontologies, User study

Functional Description: We present the GUsT-3D framework for designing Guided User Tasks in em-
bodied VR experiences, i.e., tasks that require the user to carry out a series of interactions guided by
the constraints of the 3D scene. GUsT-3D is implemented as a set of tools that support a 4-step
workflow to :

(1) annotate entities in the scene with names, navigation, and interaction possibilities, (2) define
user tasks with interactive and timing constraints, (3) manage scene changes, task progress, and
user behavior logging in real-time, and (4) conduct post-scenario analysis through spatio-temporal
queries on user logs, and visualizing scene entity relations through a scene graph.

The software also includes a set of tools for processing gaze tracking data, including: - clean
and synchronise the data - calculate fixations with I-VT, I-DT, IDTVR, IS5T, Remodnav, and IDVT
algorithms - visualize the data (points of regard and fixations) in both real time and collectively

URL: https://project.inria.fr/creattive3d/gust-3d/

Contact: Hui-Yin Wu

Participants: Hui-Yin Wu, Marco Alba Winckler, Lucile Sassatelli, Florent Robert

Partner: I3S

6.1.13 IndeGx

Keywords: Semantic Web, Indexation, Metadata

Functional Description: IndeGx is a framework for the creation of an index of a set of SPARQL endpoints.
The framework relies only on available semantic web technologies and the index appears as an
RDF database. The index is primarily composed of the self-description available in the endpoint.
This original description is verified and expanded by the framework, using SPARQL queries.

URL: https://github.com/Wimmics/dekalog

Contact: Pierre Maillot

Participants: Fabien Gandon, Catherine Faron, Olivier Corby, Franck Michel

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Inria Annual Report 2023

6.1.14 ISSA-pipeline

Name: Processing pipeline of the ISSA project

Keywords: Indexation, Semantic Web, Natural language processing, Knowledge graph, Open Access,

Open data, LOD - Linked open data

Functional Description: See the description at https://github.com/issa-project/issa-pipeline/tree/main/pipeline

Release Contributions: https://github.com/issa-project/issa-pipeline/releases/tag/2.0

URL: https://github.com/issa-project/issa-pipeline

Contact: Franck Michel

Partners: CIRAD, IMT Mines Alès

6.1.15 ISSA Visualization Web Application

Keywords: Open Access, Data visualization, Knowledge graph, NLP

Functional Description: The ISSA project focuses on the semantic indexing of scientific publications
in an open archive. The ISSA Visualization Web Application is a React and node.js based web
application meant to search articles from the ISSA knowledge base using the rich semantics of the
reference vocabularies, and provide a visualization of their metadata. This application consists of a
frontend and a backend hosted on separate repositories: https://github.com/issa-project/web-
visualization/ https://github.com/issa-project/web-backend

URL: https://github.com/issa-project/web-visualization/

Contact: Franck Michel

Partners: CIRAD, IMT Mines Alès

6.1.16 KartoGraphI

Keywords: Semantic Web, LOD - Linked open data

Functional Description: Website displaying a screenshot of the state of the Linked Data web according

to the description retrieved by the IndeGx software

URL: http://prod-dekalog.inria.fr/

Publication: hal-03652865

Contact: Pierre Maillot

6.1.17 Licentia

Keywords: Right, License

Scientific Description: In order to ensure the high quality of the data published on the Web of Data,
part of the self-description of the data should consist in the licensing terms which specify the
admitted use and re-use of the data by third parties. This issue is relevant both for data publication
as underlined in the “Linked Data Cookbook” where it is required to specify an appropriate license
for the data, and for the open data publication as expressing the constraints on the reuse of the
data would encourage the publication of more open data. The main problem is that data producers
and publishers often do not have extensive knowledge about the existing licenses, and the legal
terminology used to express the terms of data use and reuse. To address this open issue, we present
Licentia, a suite of services to support data producers and publishers in data licensing by means
of a user-friendly interface that masks to the user the complexity of the legal reasoning process.

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In particular, Licentia offers two services: i) the user selects among a pre-defined list those terms
of use and reuse (i.e., permissions, prohibitions, and obligations) she would assign to the data
and the system returns the set of licenses meeting (some of) the selected requirements together
with the machine readable licenses’ specifications, and ii) the user selects a license and she can
verify whether a certain action is allowed on the data released under such license. Licentia relies
on the dataset of machine-readable licenses (RDF, Turtle syntax, ODRL vocabulary and Creative
Commons vocabulary) available at http://datahub.io/dataset/rdflicense. We rely on the deontic
logic presented by Governatori et al. to address the problem of verifying the compatibility of the
licensing terms in order to find the license compatible with the constraints selected by the user. The
need for licensing compatibility checking is high, as shown by other similar services (e.g., Licensius5
or Creative Commons Choose service6 ). However, the advantage of Licentia with respect to these
services is twofold: first, in these services compatibility is pre-calculated among a pre-defined and
small set of licenses, while in Licentia compatibility is computed at runtime and we consider more
than 50 heterogeneous licenses, second, Licentia provides a further service that is not considered
by the others, i.e., it allows to select a license from our dataset and verify whether some selected
actions are compatible with such license.

Functional Description: Licentia is a web service application with the aim to support users in licensing
data. Our goal is to provide a full suite of services to help in the process of choosing the most
suitable license depending on the data to be licensed.

The core technology used in our services is powered by the SPINdle Reasoner and the use of
Defeasible Deontic Logic to reason over the licenses and conditions.

The dataset of RDF licenses we use in Licentia is the RDF licenses dataset where the Creative
Commons Vocabulary and Open Digital Rights Language (ODRL) Ontology are used to express the
licenses.

URL: http://licentia.inria.fr/

Contact: Serena Villata

Participant: Cristian Cardellino

6.1.18 SPARQL Micro-services

Name: SPARQL micro-services

Keywords: Web API, SPARQL, Microservices, LOD - Linked open data, Data integration

Functional Description: The approach leverages the micro-service architectural principles to define the
SPARQL Micro-Service architecture, aimed at querying Web APIs using SPARQL. A SPARQL micro-
service is a lightweight SPARQL endpoint that typically provides access to a small, resource-centric
graph. Furthermore, this architecture can be used to dynamically assign dereferenceable URIs to
Web API resources that do not have URIs beforehand, thus literally “bringing” Web APIs into the
Web of Data. The implementation supports a large scope of JSON-based Web APIs, may they be
RESTful or not.

URL: https://github.com/frmichel/sparql-micro-service

Publications: hal-02060966, hal-01722792, hal-01947589, hal-02168164

Author: Franck Michel

Contact: Franck Michel

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Inria Annual Report 2023

6.1.19 Metadatamatic

Keywords: RDF, Semantic Web, Metadata

Functional Description: Website offering a form to generate in RDF the description of an RDF base.

URL: https://wimmics.github.io/voidmatic/

Contact: Pierre Maillot

Participants: Fabien Gandon, Franck Michel, Olivier Corby, Catherine Faron

6.1.20 MGExplorer

Name: Multivariate Graph Explorer

Keyword: Information visualization

Scientific Description: MGExplorer (Multidimensional Graph Explorer) allows users to explore different
perspectives to a dataset by modifying the input graph topology, choosing visualization techniques,
arranging the visualization space in meaningful ways to the ongoing analysis and retracing their
analytical actions. The tool combines multiple visualization techniques and visual querying while
representing provenance information as segments connecting views, which each supports selection
operations that help define subsets of the current dataset to be explored by a different view. The
adopted exploratory process is based on the concept of chained views to support the incremental
exploration of large, multidimensional datasets. Our goal is to provide visual representation of
provenance information to enable users to retrace their analytical actions and to discover alternative
exploratory paths without loosing information on previous analyses.

Functional Description: MGExplorer is an information visualization tool suite that integrates many
information visualization techniques aimed at supporting the exploration of multivariate graphs.
MGExplorer allows users to choose and combine the information visualization techniques creating
a graph that describes the exploratory path of dataset. It is an application based on the D3.JS library,
which is executable in a web browser. The use of MGExplorer requires a customization to connect
the dashboard to a SPARQL endpoint. MGExplorer has been customized to facilitate the search of
scientific articles related to covid.

Release Contributions: Visualization of data extracted from linked data datasets.

URL: http://covid19.i3s.unice.fr:8080/

Publications: hal-03292172, hal-03404572, hal-03404580

Contact: Marco Alba Winckler

Participants: Aline Menin, Marco Alba Winckler, Olivier Corby

Partner: Universidade Federal do Rio Grande do Sul

6.1.21 Morph-xR2RML

Name: Morph-xR2RML

Keywords: RDF, Semantic Web, LOD - Linked open data, MongoDB, SPARQL

Functional Description: The xR2RML mapping language that enables the description of mappings from

relational or non relational databases to RDF. It is an extension of R2RML and RML.

Morph-xR2RML is an implementation of the xR2RML mapping language, targeted to translate data
from the MongoDB database, as well as relational databases (MySQL, PostgreSQL, MonetDB). Two
running modes are available: (1) the graph materialization mode creates all possible RDF triples at

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21

once, (2) the query rewriting mode translates a SPARQL 1.0 query into a target database query and
returns a SPARQL answer. It can run as a SPARQL endpoint or as a stand-alone application.

Morph-xR2RML was developed by the I3S laboratory as an extension of the Morph-RDB project
which is an implementation of R2RML.

URL: https://github.com/frmichel/morph-xr2rml/

Publications: hal-01207828, hal-01330146, hal-01280951

Author: Franck Michel

Contact: Franck Michel

6.1.22 Muvin

Name: Multimodal Visualization of Networks

Keywords: Data visualization, Music, LOD - Linked open data

Functional Description: Muvin supports the exploration of a two-layer network describing the collabor-
ations between artists and the discography of an artist, defined by the albums and songs released
by the artist over time. It implements an incremental approach, allowing the user to dynamically
import data from a SPARQL endpoint to the exploration flow. Furthermore, this approach seeks to
improve user perception by associating audio to the visualization, in a way that the users can listen
to the songs visually represented in their screen.

URL: http://dataviz.i3s.unice.fr/muvin/

Contact: Aline Menin

6.1.23 wam-studio

Keywords: Web Application, Web API, Audio signal processing

Functional Description: WAM Studio is an open source online Digital Audio Workstation (DAW) that

takes advantage of a number of APIs and standard W3C technologies, such as Web Audio, WebAssembly,
Web Components, Web Midi, Media Devices and more. WAM Studio is also based on the Web
Audio Modules (WAM) standard, which was designed to facilitate the development of interoper-
able audio plug-ins (effects, virtual instruments, virtual piano keyboards as controllers, etc.) - a
kind of "VSTs for the Web". DAWs are feature-rich software programs, and therefore particularly
complex to develop in terms of design, implementation, performance and ergonomics. Today, the
majority of online DAWs are commercial, while the only open source examples lack functional-
ity (no plug-in support, for example) and don’t take advantage of the recent possibilities of web
browsers (such as WebAssembly). WAM Studio was designed as a technology demonstrator to
promote the possibilities offered by recent innovations proposed by the W3C. Developing it was
a challenge, as we had to take into account the limitations of sandboxed and constrained envir-
onments such as Web browsers, and compensate for latency when we can’t know what hardware
is being used, etc.). An online demo and a GitHub repository for the source code are available
(https://wam-studio.i3s.univ-cotedazur.fr/).

URL: https://wam-bank.i3s.univ-cotedazur.fr/

Contact: Michel Buffa

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Inria Annual Report 2023

6.1.24 WebAudio tube guitar amp sims CLEAN, DISTO and METAL MACHINEs

Name: Tube guitar amplifier simulators for Web Browser : CLEAN MACHINE, DISTO MACHINE and

METAL MACHINE

Keyword: Tube guitar amplifier simulator for web browser

Scientific Description: This software is one of the only ones of its kind to work in a web browser. It uses
"white box" simulation techniques combined with perceptual approximation methods to provide a
quality of guitar playing in hand comparable to the best existing software in the native world.

Functional Description: Software programs for creating real-time simulations of tube guitar amplifiers
that behave most faithfully like real hardware amplifiers, and run in a web browser. In addition,
the generated simulations can run within web-based digital audio workstations as plug-ins. The
"CLEAN MACHINE" version specializes in the simulation of acoustic guitars when playing electric
guitars. The DISTO machine specializes in classic rock tube amp simulations, and METAL MA-
CHINE targets metal amp simulations. These programs are one of the results of the ANR WASABI
project.

Release Contributions: First stable version, delivered and integrated into the ampedstudio.com software.

Two versions have been delivered: a limited free version and a commercial one.

Publications: hal-01721463, hal-01893681, hal-02337828, hal-03087768, hal-01721483, hal-01735478,

hal-02366725, hal-02557901, hal-01589330, hal-03087763, hal-01893660, hal-01589229

Contact: Michel Buffa

Participant: Michel Buffa

Partner: Amp Track Ltd, Finland

6.2 Open data

6.2.1 CyberAgressionAdo

Participants:

Anaïs Ollagnier, Serena Villata, Elena Cabrio.

Name: CyberAgressionAdo-v1
Description: The CyberAgressionAdo-v1 dataset comprises 19 instances of aggressive multiparty
chats in French, gathered through a role-playing game conducted in high schools. This dataset is built
upon scenarios that emulate cyber aggression situations prevalent among teenagers, addressing sensitive
topics like ethnic origin, religion, or skin color. The recorded conversations have undergone meticulous
annotation, taking into account various facets, including participant roles, the occurrence of hate speech,
the nature of verbal abuse within the messages, and the identification of humor devices like sarcasm or
irony in utterances.

The guidelines and data are provided on a a dedicated repository.
Release Contributions: CyberAgressionAdo-V2 uses a multi-label, fine-grained tagset marking the
discursive role of exchanged messages as well as the context in which they occur – for instance, attack
(ATK), defend (DFN), counterspeech (CNS), abet/instigate (AIN), gaslight (GSL), etc.

The guidelines and data are provided on a a dedicated repository.
Publications: [113]
Contact: Anaïs Ollagnier

Project WIMMICS

6.2.2 ElecDeb60to20

Participants:

Serena Villata, Elena Cabrio, Pierpaolo Goffredo.

23

Name: ElecDeb60to20
Description: The ElecDeb60to20 dataset is built from the official transcripts of the televised presiden-
tial debates in the US from 1960 until 2020, from the website of the Commission on Presidential Debates
(CPD). These political debates are manually annotated with argumentative components (claim, premise)
and relations (support, attack). In addition, it also includes the annotation of fallacious arguments, based
on the following 6 classes of fallacies: ad hominem, appeal to authority, appeal to emotion, false cause,
slogan, slippery slope.

The guidelines and data are provided on a a dedicated repository.
Publications: [62], [63]
Contact: Serena Villata

6.2.3 ISSA Agritrop Dataset

Participants:

Anna Bobasheva, Catherine Faron, Franck Michel.

Name: ISSA Agritrop Dataset
Description: The ISSA Agritrop Dataset was produced in the context of the ISSA 2 project. It provides a
semantic index of the articles of the Agritrop scientific archive. It is built by extracting thematic descriptors
and named entities from the articles’ text and linking them with resources from DBpedia, Wikidata, the
AGROVOC thesaurus and GeoNames.

The data model and generation pipeline are provided on a dedicated repository (DOI: 10.5281/zen-

odo.10376913).

Release Contributions: version 2.0 extends the dataset with the RDF representation of additional
data, and the result of processing 13.000+ full-text published articles, as well as 10.000+ abstracts from
other documents that were not processed before.

DOI: 10.5281/zenodo.10381606
Publications: [44], [114]
Contact: Franck Michel

6.2.4 WeKG-MF

Participants: Nadia Yacoubi Ayadi, Catherine Faron, Franck Michel.

Name: Weather Knowledge Graph of Météo France Meteorological Observations
Description: WeKG-MF represents the meteorological observations made by 62 Météo-France
weather stations located in different regions in metropolitan France and overseas departments, from 2012
to 2022. This work is supported by the French National Research Agency under grant ANR-18-CE23-0017
(project D2KAB). The raw data was obtained from Météo France’s website. The data model and generation
pipeline are provided on a dedicated repository.

Release Contributions:
DOI: 10.5281/zenodo.10420888
Publications: [83], [115], [116]
Contact: Franck Michel

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Inria Annual Report 2023

6.2.5 WheatGenomicsSLKG

Participants: Nadia Yacoubi Ayadi, Catherine Faron, Franck Michel.

Name: Wheat Genomics Scientific Literature Knowledge Graph
Description: The Wheat Genomics Scientific Literature Knowledge Graph is a FAIR knowledge graph
that exploits the Semantic Web technologies to integrate information about Named Entities (NE) extracted
automatically from a corpus of PubMed scientific papers on wheat genetics and genomics. This work is
supported by the French National Research Agency under grant ANR-18-CE23-0017 (project D2KAB). The
data model and generation pipeline are provided on a dedicated repository.

Release Contributions:
DOI: 10.5281/zenodo.10420888
Publications:
Contact: Franck Michel

6.2.6 Pharmacogenomics datasets for Ontology Matching

Name: Pharmacogenomics datasets for Ontology Matching

Participants:

Pierre Monnin.

Description: These datasets constitute benchmarks to evaluate Ontology Matching algorithms on
a complex structure-based instance matching task from the domain of pharmacogenomics. Pharma-
cogenomics involves n-ary tuples representing so-called “pharmacogenomic relationships” and their
components of three distinct types: drugs, genetic factors, and phenotypes. The goal resides in matching
such tuples. These datasets were extracted from the PGxLOD knowledge graph.

Release Contributions:
DOI: 10.5281/zenodo.8419361
Publications:
Contact: Pierre Monnin

6.2.7 DBpedia.fr : French chapter of the DBpedia knowledge graph dataset

Name: DBpedia.fr

Participants:

Fabien Gandon, Franck Michel, Célian Ringwald.

The DBpedia.fr project ensures the creation and maintenance of a French chapter of the DBpedia
knowledge base a crowd-sourced community effort to extract structured content from the information
created in various Wikimedia projects.

No new release was done this year but we carried out continuous monitoring and support to ensure a

high-availability service.

Statistics indicate very high usage rate: the server processed 1.8+ billion queries over the year. This

represents a 3.86 million daily average and 32.5 million daily max.

URL: http://dbpedia.fr
Contact: Célian Ringwald, Franck Michel, Fabien Gandon

Project WIMMICS

7 New results

7.1 User Modeling and Designing Interaction

7.1.1 Spatio-Temporal Data Visualization

Participants:

Aline Menin, Marco Winckler, Franck Michel.

25

Knowledge graph visualization is a challenge on itself specially due the presence of multidimensional
data: as the number data dimensions increases, a single view displaying all the information at once is no
longer suitable due to cognitive overload and visual cluttering [112]. A frequent type of multidimensional
data in many datasets concerns spatio-temporal data. When designing visualizations to represent
spatio-temporal data, one must respect the order of time units and the geographical location of data
records, which hinders the design process. In this context, we investigate the use of glyph-based data
representations which might be suitable to convey multiple attributes in a small and compact visual
representation. We have chosen glyphs because (i) they can be used independently and constructively
to depict attributes of a data record or the composition of a set of data records; (ii) they can be spatially
connected to convey topological relationships between data records; and (iii) they are visual signs that
can make use of visual features of other visual signs such as icons, indices and symbols [108]. Whilst
most of visualization techniques use static representations of glyphs, we propose the use of polymorphic
glyphs (i.e. glyphs that change its shape according to the surrounding semantics and/or user actions)
to assist the exploration of hierarchical temporal data [70]. As a proof-of-concept, we implemented our
approach in the form of a web-based visualization tool, called PHiTGlyph (Polymorphic and HIerarchic
Temporal Glyph), that uses interaction to activate the polymorphous aspect of glyphs. We have applied the
approach to explore hierarchical data over space and time. In particular, we demonstrate the suitability
of our approach by exploring the ISSA KG, which describes scholarly articles in the field of agriculture.
The tool is available here.

7.1.2 Tracking and Exploring Audiovisual Archives about the European Union

Participants:

Aline Menin, Marco Winckler, Shiming Shen, Matteo Treleani, Jean-
Marie Dormoy.

As part of the project Crossing Borders Archives (CROBORA) we investigate the use of visual queries
and visualization techniques as a mean to explore large collections of audiovisual documents within
television and web archives. One of the main goals of the project CROBORA is to identify the use of
sequences of images/videos that are repeatedly used to illustrated a discourse within a corpus of thou-
sands of videos). In fact, any audiovisual sequence reused within different contexts exists conceptually
as the repetition of one single visual unit, but also from the point of view of the metadata tagging its
occurrences, each item is a distinct document. Our contribution is mainly focused on the support to
the retrieval and analysis of stock shots collected on TV news broadcasted from the period 2001 - 2020
in major national channels in France and in Italy (TF1, France 2, and ARTE on the French side, and
Rai 1, Rai 2, and Canale 5 on the Italian one). The resulting dataset contains over 27,000 stock shots
that were manually annotated to describe their usage, i.e. either representing an event, a celebrity, a
place or used as illustration [42]. The volume and multidimensionality of the corpus makes it impossible
for a human to explore it without technological help. We thus investigate the usage of visualization
techniques to support the domain experts on analyzing the data and finding the relationship between
stock shots and their usage throughout time. We have developed a tool that supports stock shots search
and visualization through multiple perspectives: hierarchically, temporal distribution and association
through their co-occurrence in the same television archive [43]. The tool is available here.

7.1.3 Interaction with extended reality

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Participants:

Aline Menin, Clément Quéré, Florent Robert, Hui-Yin Wu,
Marco Winckler.

Virtual reality (VR) and Augmented Reality (AR) offer extraordinary opportunities to create immersive
or mixed-reality experiences allowing users to explore relations (patterns, trends, clusters) in information
distributed in a 3D space. The feeling of immersion created by such a technology is expected to foster
the user cognition thus helping to make better decisions that require an analysis of spatial information.
A major challenge of designing these 3D experiences and user tasks, however, lies in bridging the inter-
relational gaps of perception between the designer, the user, the information communicated, and the
3D scene. In the context of the PhD thesis of Florent Robert we have started a series of studies aiming to
understand how the design of user of 3D scenes affects the user perception and how such perception
affects decision-making processes [79]. Our ultimate goal is to understand how the many components of
the user interaction (including user attention) might affect the embodied experience virtual immersive
environments. Moreover, we want to explore how metadata information (attached to virtual objects)
might help users to navigate in the immersive environment and how such information affects users’
decisions. We have proposed a tool, called GUsT-3D framework, for designing Guided User Tasks in
embodied VR experiences, i.e., tasks that require the user to carry out a series of interactions guided by the
constraints of the 3D scene. Whilst in the PhD thesis of Florent Robert, we mainly explore 3D scenes that
are a recreation of parts of the real-world, in the PhD thesis of Clément Quéré, we use VR/AR technologies
to explore visualizalition of more abstract volumes of spatio-temporal datasets. In cooperation with the
IMREDD we are developing an immersive environment that allows to visualize the multidimensional
sensors’ data (ex. traffic, weather forecast, pollution...) that describe the evolution of smart cities’ states.
Another ongoing work concerns the study of augmented reality tools that might be used to annotate both
virtual and real-life objects, thus increasing the semantics of objects annotated and reducing the gap
between digital and real-life objects.

7.1.4 Incremental visual exploration of linked data

Participants: Marco Winckler, Aline Menin, Olivier Corby, Catherine Faron,

Alain Giboin.

Information visualization techniques are useful to discover patterns and causal relationships within
LOD datasets. However, since the discovery process is often exploratory (i.e., users have no predefined
goal and do not expect a particular outcome), when users find something interesting, they should be
able to (i) retrace their exploratory path to explain how results have been found, and (ii) branch out the
exploratory path to compare data observed in different views or found in different datasets. Furthermore,
as most of LOD datasets are very specialized, users often need to explore multiple datasets to obtain
the knowledge required to support decision-making processes. Thus, the design of visualization tools is
confronted with two main challenges: the visualization system should provide multiple views to enable
the exploration of different or complementary perspectives to the data; and the system should support
the combination of diverse data sources during the exploration process. To our knowledge, the existing
tools before our work, are limited to visualizing a single dataset at a time and, often, use static and
preprocessed data. Thus, we proposed the concept of follow-up queries to allow users to create queries
on demand during the exploratory process while connecting multiple LOD datasets with chained views.
Our approach relies on a exploration process supported by the use of predefined SPARQL queries that the
user can select on-the-fly to retrieve data from different SPARQL endpoints. It enables users to enrich the
ongoing analysis by bringing external and complementary data to the exploration process, while also
supporting the visual analysis and comparison of different subsets of data (from the same or different
SPARQL endpoints) and, thus, the incremental exploration of the LOD cloud. The resulting publication
[92] presents a generic visualization approach to assist the analysis of multiple LOD datasets based on
the concepts of chained views and follow-up queries. We demonstrate the feasibility of our approach via
four use case scenarios and a formative evaluation where we explore scholarly data described by RDF
graphs publicly available through SPARQL endpoints. These scenarios demonstrate how the tool supports

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(i) composing, running, and visualizing the results of a query; (ii) subsetting the data and exploring it
via different visualization techniques; (iii) instantiating a follow-up query to retrieve external data; and
(iv) querying a different database and compare datasets. The usability and usefulness of the proposed
approach is confirmed by results obtained with a series of semi-structured interviews. The results are
encouraging while showing the relevance of the approach to explore big linked data. This work resulted
in a visualization tool, called LDViz, publicly accessible at dataviz.i3s.unice.fr/ldviz. The source code is
also open and published as 10.5281/zenodo.6511782. A large study concerning the scalability of the tools
has demonstrated over 420 public end points [92].

7.1.5 Development of an extensible User Profile model

Participants:

Ankica Bariši´c, Marco Winckler.

During the post-doc of Ankica Bariši´c (from January to December 2023), we have investigated the
evolving landscape of user modeling in response to technological advances and increasing complexities
in interaction systems. The aims was to develop an extensible User Profile model that could be seamlessly
used for the development of complex interactive systems. The objectives include acquiring knowledge
of various user modeling techniques, distinguishing between short-term and long-term user models,
and developing adaptive models across different phases of system development and operation. We
investigated the use of ontologies as a mean to ensure interoperability and facilitate data federation
in representing user profiles. We systematically analyzed existing ontologies for user profiles, such as
GUMO, UPO, OntobUMf, HPO, PO, Grapple ontology, and User Modeling Meta-Ontology. In order
to overcome the lack of flexibility to extend the attribute of existing user profile ontologies, we have
developed the User Profile Meta-Ontology (UPMO) which is rooted in Knowledge Graphs (RDF) and
MDE principles [90]. The UMPO played a pivotal role in facilitating the sharing and reuse of domain
knowledge while streamlining the analysis and extraction of domain-specific information. We also have
investigated methods for obtaining user profiles, encompassing qualitative and quantitative research
methods; we have focused on three prevalent approaches: role-based, persona development, and user
profiling. The UPMO was showcased as a foundational element guiding developers through various
phases of system development, promoting consistency, interoperability, and efficiency. Our approach
was applied in automobile domain, leading to a scientific contribution in terms of a driver model tailored
for Take-Over-Request (TOR) scenarios in autonomous vehicles [54].

7.1.6 Interactive WebAudio applications

Participants: Michel Buffa, Shihong Ren.

During the WASABI ANR research project (2017-2020), we built a 2M song database made of metadata
collected from the Web of Data and from the analysis of song lyrics of the audio files provided by Deezer.
This dataset is still exploited by current projects inside the team (in particular by the PhD of Maroua
Tickat). Other initiatives closely related to the WASABI datasets include several Web Audio interactive
applications and frameworks. The Web Audio Modules 2.0, a WebAudio plugin standard for developing
high performance plugins in the browser published for the first time in 2021, has encoutered a large
adoption by researchers and developers [57, 105]. The open source Wam-Studio Digital Audio Workstation
developed by Michel Buffa and Antoine Vidal-Mazuy led to a scientific collaboration with the MERI team
from the CCRMA Laboratory in Stanford [88, 56, 55]. The team is also participating to the ANR DOTS
project [47], that aims to produce distributed music performances using Web Audio based infrastructure.
We also developed new methods for real-time tube guitar amplifier simulations that run in the browser
[33, 91]. Some of these results are still unique in the world as in 2023, and have been acclaimed by several
awards in international conferences. The guitar amp simulations are now commercialized by the CNRS
SATT service and are available in the online collaborative Digital Audio Workstation ampedstudio. Some

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Inria Annual Report 2023

other tools we designed are linked to the WASABI knowledge base, that allow, for example, songs to be
played along with sounds similar to those used by artists. An ongoing PhD proposes a visual language for
music composers to create instruments and effects linked to the WASABI corpus content and a research
collaboration with the Shangai conservatory of Music is also being pursued about the generation of music
on the Web with real-time Brain Wave [78].

7.1.7 KartoGraphI: Drawing a Map of Linked Data

Participants:

Pierre Maillot, Olivier Corby, Catherine Faron, Fabien Gandon,
Franck Michel.

A large number of semantic Web knowledge bases have been developed and published on the Web. To
help the user identify the knowledge bases relevant for a given problem, and estimate their usability, we
propose a declarative indexing framework and an associated visualization Web application, KartoGraphI.
It provides an overview of important characteristics for more than 400 knowledge bases including, for
instance, dataset location, SPARQL compatibility level, shared vocabularies, etc. [38]

7.2 Communities and Social Interactions Analysis

7.2.1 Autonomous agents in a social and ubiquitous Web

Participants:

Andrei Ciortea, Olivier Corby, Fabien Gandon, Franck Michel.

Recent W3C recommendations for the Web of Things (WoT) and the Social Web are turning hyper-
media into a homogeneous information fabric that interconnects heterogeneous resources: devices,
people, information resources, abstract concepts, etc. The integration of multi-agent systems with such
hypermedia environments now provides a means to distribute autonomous behavior in worldwide
pervasive systems.

A central problem then is to enable autonomous agents to discover heterogeneous resources in world
wide and dynamic hypermedia environments. This is a problem in particular in WoT environments that
rely on open standards and evolve rapidly—thus requiring agents to adapt their behavior at runtime
in pursuit of their design objectives. To this end, we developed a hypermedia search engine for the
WoT that allows autonomous agents to perform approximate search queries in order to retrieve relevant
resources in their environment in (weak) real time. The search engine crawls dynamic WoT environments
to discover and index device metadata described with the W3C WoT Thing Description, and exposes
a SPARQL endpoint that agents can use for approximate search. To demonstrate the feasibility of our
approach, we implemented a prototype application for the maintenance of industrial robots in worldwide
manufacturing systems. The prototype demonstrates that our semantic hypermedia search engine
enhances the flexibility and agility of autonomous agents in a social and ubiquitous Web [109].

More generaly, we defend that Knowledge Graphs (KGs) are one of the prime approaches to support
the programming of autonomous software systems at the knowledge level [34]. We are working towards
defining a new class of Web-based Multi-Agent Systems (MAS) that can inherit the architectural properties
of the Web (scalability, heterogeneity, evolvability, etc.), preserve the architectural properties of MAS
(adaptability, openness, robustness, etc.), and are human-centric (usable, transparent, accountable,
etc.) [46]. We study the idea of decentralized hypermedia applications in general [45] and consider the
abstractions needed for designing multi-agent systems on the Web [48], the challenges in achieving
Linked Multi-Agent Systems (MAS) on the Web [49] and specific problems such as the one of situatedness
and embodiment of agents acting on the Web (namely, "Web agents") [84].

7.2.2 Online Hate Detection in Conversational Data

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Participants:

Elena Cabrio, Serena Villata, Anais Ollagnier.

Online Hate Detection (OHD) encompasses various sub-tasks, including the classification of hateful
content (e.g., distinguishing hate and non-hate speech, identifying hate targets, or detecting types of
hate). More recently, the focus has extended to the task of Participant Role Identification (PRI). In PRI,
the objective is to identify different participant roles involved in cyberbullying episodes, such as bully,
victim, bystander, assistant, defender, among others. While existing research has predominantly targeted
social networks like Twitter and Instagram, the significant role of private instant messaging platforms in
facilitating bullying, particularly among teens, has gained attention.

Despite the prevalence of bullying on private messaging platforms, few studies have delved into
the task of PRI in multi-party chats due to the privacy policies of these platforms. Leveraging the
CyberAgressionAdo-V1 dataset [113], collected under the UCA IDEX OTESIA project "Artificial Intelligence
to prevent cyberviolence, cyberbullying, and hate speech online" 1, we aim to address this gap. Our
contribution involves introducing a pipeline designed to automate the identification of participant roles
in cyberbullying episodes occurring within multi-party chats [75]. Additionally, we have developed a fully
web-based platform for participant role detection in multi-party chats, featuring a novel role scoring
function [76].

A conference paper presenting the CyberAgressionAdo-V2 dataset is currently under submission.
This enhanced version of CyberAgressionAdo-V1 introduces a new tagset that incorporates markers for
pragmatic-level information observed in cyberbullying situations. The refined tagset comprises six layers,
with two specifically focusing on pragmatic considerations, capturing both intentions and context. These
layers aim to grasp the authors’ intentions and establish the context in which messages serve as responses.
This paper delivers a three-fold contribution: (1) it advances the development of solutions addressing
diversity in digital harassment, (2) it capitalizes on annotators’ disagreements in tagset development, and
(3) it delves into pattern mining to enhance the modeling of intricate communication patterns prevalent
in cyberbullying.

7.2.3 Hate Speech Target Community Detection

Participants:

Elena Cabrio, Serena Villata, Anais Ollagnier.

Pursuing the ongoing endeavor in reaching a fine-grained online hate speech characterization to
provide appropriate solutions to curb online abusive behaviors, we proposed a full pipeline that enables
to capture targeting characteristics in hatred contents (i.e., types of hate, such as race and religion) aiming
at improving the understanding on how hate is conveyed on Twitter [39]. Our contribution is threefold:
(1) we leverage multiple data views of a different nature to contrast different kinds of abusive behaviors
expressed towards targets; (2) we develop a full pipeline relying on a multi-view clustering technique
to address the task of hate speech target characterization; and (3) we propose a methodology to assess
the quality of generated hate speech target communities. Relying on multiple data views built from
multilingual pre-trained language models (i.e., multilingual BERT and multilingual Universal Sentence
Encoder) and the Multi-view Spectral Clustering (MvSC) algorithm, the experiments conducted on a
freely available multilingual dataset of tweets (i.e., the MLMA hate speech dataset) show that most of the
configurations of the proposed pipeline significantly outperform state-of-the-art clustering algorithms
on all the tested clustering quality metrics on both French and English.

7.2.4 An In-depth Analysis of Implicit and Subtle Hate Speech Messages

Participants: Nicolás Benjamín Ocampo, Ekaterina Sviridova, Elena Cabrio, Ser-

ena Villata.

1OTESIA Project Launch

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Inria Annual Report 2023

The research carried out so far in detecting abusive content in social media has primarily focused
on overt forms of hate speech. While explicit hate speech (HS) is more easily identifiable by recognizing
hateful words, messages containing linguistically subtle and implicit forms of HS (as circumlocution,
metaphors and sarcasm) constitute a real challenge for automatic systems. While the sneaky and tricky
nature of subtle messages might be perceived as less hurtful with respect to the same content expressed
clearly, such abuse is at least as harmful as overt abuse. In this paper, we first provide an in-depth and
systematic analysis of 7 standard benchmarks for HS detection, relying on a fine-grained and linguistically-
grounded definition of implicit and subtle messages. Then, we experiment with state-of-the-art neural
network architectures on two supervised tasks, namely implicit HS and subtle HS message classification.
We show that while such models perform satisfactory on explicit messages, they fail to detect implicit
and subtle content, highlighting the fact that HS detection is not a solved problem and deserves further
investigation [74].

7.2.5 Generating Adversarial Examples for Implicit Hate Speech Detection

Participants: Nicolás Benjamín Ocampo, Elena Cabrio, Serena Villata.

Research on abusive content detection on social media has primarily focused on explicit forms of
hate speech (HS), that are often identifiable by recognizing hateful words and expressions. Messages
containing linguistically subtle and implicit forms of hate speech still constitute an open challenge for
automatic hate speech detection. In this paper, we propose a new framework for generating adversarial
implicit HS short-text messages using Auto-regressive Language Models. Moreover, we propose a strategy
to group the generated implicit messages in complexity levels (EASY, MEDIUM, and HARD categories)
characterizing how challenging these messages are for supervised classifiers. Finally, we propose a “build
it, break it, fix it”, training scheme using HARD messages showing how iteratively retraining on HARD
messages substantially leverages state-of-the-art models’ performances on implicit HS benchmarks [72].

7.2.6 Bridging Implicit and Explicit Hate Speech Embedding Representations

Participants: Nicolás Benjamín Ocampo, Elena Cabrio, Serena Villata.

Research on automatic hate speech (HS) detection has mainly focused on identifying explicit forms
of hateful expressions on user-generated content. Recently, a few works have started to investigate
methods to address more implicit and subtle abusive content. However, despite these efforts, automated
systems still struggle to correctly recognize implicit and more veiled forms of HS. As these systems heavily
rely on proper textual representations for classification, it is crucial to investigate the differences in
embedding implicit and explicit messages. Our contribution to address this challenging task is fourfold.
First, we present a comparative analysis of transformer-based models, evaluating their performance
across five datasets containing implicit HS messages. Second, we examine the embedding representations
of implicit messages across different targets, gaining insight into how veiled cases are encoded. Third,
we compare and link explicit and implicit hateful messages across these datasets through their targets,
enforcing the relation between explicitness and implicitness and obtaining more meaningful embedding
representations. Lastly, we show how these newer representation maintains high performance on HS
labels, while improving classification in borderline cases [73].

7.2.7 Fallacious Argument Classification in Political Debates

Participants:

Pierpaolo Goffredo, Shohreh Haddadan, Vorakit Vorakitphan,
Elena Cabrio, Serena Villata.

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Fallacies play a prominent role in argumentation since antiquity due to their contribution to argu-
mentation in critical thinking education. Their role is even more crucial nowadays as contemporary
argumentation technologies face challenging tasks as misleading and manipulative information detection
in news articles and political discourse, and counter-narrative generation. Despite some work in this
direction, the issue of classifying arguments as being fallacious largely remains a challenging and an
unsolved task. Our contribution is twofold: first, we present a novel annotated resource of 31 political
debates from the U.S. Presidential Campaigns, where we annotated six main categories of fallacious
arguments (i.e., ad hominem, appeal to authority, appeal to emotion, false cause, slogan, slippery slope)
leading to 1628 annotated fallacious arguments; second, we tackle this novel task of fallacious argument
classification and we define a neural architecture based on transformers outperforming state-of-the-art
results and standard baselines. Our results show the important role played by argument components and
relations in this task [63].

7.2.8 DISPUTool 2.0: A Modular Architecture for Multi-Layer Argumentative Analysis of Political

Debates

Participants:

Pierpaolo Goffredo, Elena Cabrio, Serena Villata.

Political debates are one of the most salient moments of an election campaign, where candidates are
challenged to discuss the main contemporary and historical issues in a country. These debates represent
a natural ground for argumentative analysis, which has always been employed to investigate political
discourse structure and strategy in philosophy and linguistics. In this paper, we present DISPUTool 2.0,
an automated tool which relies on Argument Mining methods to analyze the political debates from the
US presidential campaigns to extract argument components (i.e., premise and claim) and relations (i.e.,
support and attack), and highlight fallacious arguments. DISPUTool 2.0 allows also for the automatic
analysis of a piece of a debate proposed by the user to identify and classify the arguments contained in
the text. A REST API is provided to exploit the tool’s functionalities [62].

The new version of the DISPUTool demo is publicly accessible at 3ia-demos.inria.fr/disputool/.

7.2.9 Argument-based Detection and Classification of Fallacies in Political Debates

Participants:

Pierpaolo Goffredo, Mariana Chaves Espinoza, Elena Cabrio, Ser-
ena Villata.

Fallacies are arguments that employ faulty reasoning. Given their persuasive and seemingly valid
nature, fallacious arguments are often used in political debates. Employing these misleading arguments
in politics can have detrimental consequences for society, since they can lead to inaccurate conclusions
and invalid inferences from the public opinion and the policymakers. Automatically detecting and
classifying fallacious arguments represents therefore a crucial challenge to limit the spread of misleading
or manipulative claims and promote a more informed and healthier political discourse. Our contribution
to address this challenging task is twofold. First, we extend the ElecDeb60To16 dataset of U.S. presidential
debates annotated with fallacious arguments, by incorporating the most recent Trump-Biden presidential
debate. We include updated tokenlevel annotations, incorporating argumentative components (i.e.,
claims and premises), the relations between these components (i.e., support and attack), and six cat-
egories of fallacious arguments (i.e., Ad Hominem, Appeal to Authority, Appeal to Emotion, False Cause,
Slippery Slope, and Slogans). Second, we perform the twofold task of fallacious argument detection and
classification by defining neural network architectures based on Transformers models, combining text,
argumentative features, and engineered features. Our results show the advantages of complementing
transformer-generated text representations with non-textual features [63].

7.2.10 Augmenting participation, co-creation, trust and transparency in Deliberative Democracy at

all scales through Argument Mining

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Inria Annual Report 2023

Participants: Cristian Cardellino, Elena Cabrio, Serena Villata.

ORBIS is a project2 starting in 2023 that aims to connect citizenship and policy making institutions by
providing a solution to enable the transition to a more inclusive, transparent and trustful Deliberative
Democracy in Europe. The project shapes and supports new democratic models that are developed
through deliberative democracy processes. In the context of this project, we are currently working
on the creation and annotation of a dataset of public debates for exploring semi-supervised learning
for Argumentation Mining in deliberative democracy. The proposed models will be integrated in the
DISPUTool Model.

7.2.11 Unveiling fake news through argumentative evidence

Participants:

Xiaoou Wang, Elena Cabrio, Serena Villata.

The need for automated and effective fact-checking (FC) has become urgent with the increase in
the spread of harmful content on social media. Recently, fake news classification (FNC) has evolved to
incorporate justifications provided by fact-checkers to explain their decisions, improving the transparency
of the online content assessment process. In this research line, in the context of the ATTENTION project,
we argue that an argumentative representation of fact-checkers’ justifications on why they label certain
content as fake can enhance FNC systems in terms of both precision and explainability. To address
this challenging task, we built LIARArg, a novel annotated linguistic resource composed of 2832 news
and their justifications. LIARArg extends the 6-label FNC dataset LIARPLUS with argument structures,
leading to the first FNC dataset annotated with argument components (i.e., claim and premise) and
fine-grained relations, i.e., (partial) attack and (partial) support. To integrate argumentation in FNC,
we propose a joint learning method combining argument mining and FNC which outperforms SOTA
approaches, especially for news with intermediate truthfulness labels. We demonstrate the essential
role of argument relations in enhancing the classifier performance and, more importantly, we highlight
the contribution of fine-grained relations which allow an extra performance boost. We show that the
argumentative representation of human justifications enables the explanation of the classifier assessment
in a chain-of-thought manner, paving a promising avenue for research in explainable fact-checking.

7.3 Vocabularies, Semantic Web and Linked Data Based Knowledge Representation

and Artificial Intelligence Formalisms on the Web

7.3.1 Semantic Web for Biodiversity

Participants:

Franck Michel, Catherine Faron.

This activity addresses the challenges of exploiting knowledge representation and semantic Web
technologies to enable data sharing and integration in the biodiversity area. The collaboration with the
”Muséum National d’Histoire Naturelle” of Paris (MNHN) goes on along several axes.

In 2023, the MNHN has further extended its use of the SPARQL Micro-Services architecture and
framework that we maintain, to help biologists in editing taxonomic information by confronting multiple,
heterogeneous data sources. This architecture, as well as these biodiversity use cases, was the object of a
presentation at the Knowledge Graph Conference [71], an industry-focused conference.

Furthermore, we have collaborated on the submission of a joint project on the representation of
species life traits using the Plinian core vocabulary [40] (GBIF CfP Capacity Enhancement Support
Programme). This project was accepted and will start in early 2024.

2ORBIS Web Site

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7.3.2 Semantic Web for Life Sciences

Participants:

Pierre Monnin.

Life sciences produce and consume vast amounts of scientific data. The graph-structured nature of
these data naturally leads to data-driven research efforts leveraging Semantic Web and Knowledge Graph
technologies. In a survey and position paper, we discussed recent developments and advances in the use
of graph-based technologies in life sciences and set out a vision for how these technologies will impact
these fields into the future [35].

Among such usages, knowledge graph construction and management is a well established topic. One
subtask lies in matching similar or related units across datasets to identify possible overlaps. In this
direction, we proposed the new track “Pharmacogenomics” in the international challenge “Ontology
Alignement Evaluation Initiative”. This track focuses on the matching of pharmacogenomic knowledge
units, which are n-ary tuples involving components of three distinct types (drugs, genetic factors, and
phenotypes). None of the approaches participating in the 2023 campaign were able to produce align-
ments, which highlights the importance of the research question behind this task and the need to develop
appropriate approaches or enrich existing ones [77].

7.3.3 Evolutionary agent-based evaluation of the sustainability of different knowledge sharing

strategies in open multi-agent systems

Participants:

Stefan Sarkadi, Fabien Gandon, Andrea Tettamanzi.

The advancement of agent technologies and their deployment in various fields of application has
brought numerous benefits w.r.t. knowledge or data gathering and processing. However, one of the
key challenges in deploying artificial intelligent agents in an open environment like the Web is their
interoperability. Even tough research and development of agent technologies on the Semantic Web has
advanced significantly, artificial agents live on the Web in silos, that is in very limited domains, isolated
from other systems and agents that live on the Web. In this work we setup a simulation framework and
evaluation based on evolutionary agent-based modeling to empirically test how sustainable different
strategies are for knowledge sharing in open multi-agent systems and to see which of these strategies
could actually enable global interoperability between Web agents. The first results are showing the
interest of translation-based approaches and the need for further incentives to support these [80].

7.3.4 W3C Data activity and AC Rep

Participants:

Rémi Ceres, Pierre-Antoine Champin, Fabien Gandon, Franck Michel,
Olivier Corby.

Semantic Web technologies are based on a set of standards developed by the World Wide Web
consortium (W3C). Participation in these standardization groups gives to researcher the opportunity to
promote their results towards a broad audience, and to keep in touch with an international community
of experts. Wimmics has a long history of being involved in W3C groups.

As W3C fellow, Pierre-Antoine Champin also works within the W3C team to support Semantic Web
related working groups and promote the emergence of new ones, to ensure the necessary evolutions of
these technologies. Two new groups were created in 2022, where Wimmics members are largely involved.
RDF Canonicalization and Hash aims at providing a way to hash RDF data independently of the way they
are represented. RDF-star is chartered to publish the new version of RDF and SPARQL, extending them
with the ability to make statements about statements.

Furthermore, Wimmics has been an active member of the Knowledge Graph Construction Community
Group since its creation in 2020. In 2023, a first major outcome of this group was the publication of the

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Inria Annual Report 2023

RDF Mapping Language (RML) Ontology, a modular redesign of the first RML ontology, that leverages a
decade of experience in mapping heterogeneous data to RDF [66].

The work has started towards the creation of a working group to standardize the Solid protocol. The
Solid project was started by Tim Berners-Lee, inventor of the Web, and builds on Semantic Web standards
to promote the (re-)decentralization of the Web.

Finally, Fabien Gandon remains the W3C AC Rep for Inria representing institute in all standardization

processes and W3C meetings (annual W3C TPAC conference and W3C AC Meeting).

7.3.5 AutomaZoo: Automatic annotation of an ancient Zoological Corpus

Participants:

Arnaud Barbe, Molka Dhouib, Catherine Faron.

This project is part of the scientific actions carried out by the Zoomathia international research
network which aims to study the constitution and transmission of zoological knowledge from Antiquity to
the Middle Ages. The aim of the project is to produce a corpus of textual resources semantically annotated
by a graph of ancient zoological knowledge, respecting semantic web standards, interoperable and
published on the open data web. In this context, our initial step involves constructing the ZooKG-Pliny
knowledge Graph from a manual annotation of Pliny’s Naturalis Historia using concepts gathered in
the thesaurus TheZoo. ZooKG-Pliny is based on a semantic model that formalizes knowledge about the
annotations of zoological information in texts. ZooKG-Pliny allows the integration and the interrogation
of relevant knowledge in order to support epistemologists, historians and philologists in their analysis of
these texts and knowledge transmission through them [85, 102].

The second task includes providing semantic annotations to the paragraphs of the ancient zoological
text Naturalis Historia (Pliny the Elder) according to the concepts in the domain thesaurus TheZoo. For
that, we consider two approaches to automatically classify paragraphs of Pliny’s Naturalis Historia on
ancient zoology into one or more macro collections of concepts (i.e. "Places", "Anthroponym", etc.) from
the TheZoo thesaurus: (i) The baseline method consists into training a Support Vector Machine for each
collection separately. (ii) The knowledge-based method extends the baseline by using the hierarchical
information extracted from the thesaurus [81].

7.3.6 Contributions to Agronomical knowledge graphs

Participants: Nadia Yacoubi Ayadi, Catherine Faron, Franck Michel, Olivier Corby,

Fabien Gandon.

The interest of the agronomy and biodiversity communities in the development of crop models
coupled to weather and climate models has led to the need for datasets of meteorological observations
in which data are semantically described and integrated. For this purpose, in 2022, we proposed a
semantic model to represent and publish meteorological observational data as Linked Data. Our model
reuses a network of existing ontologies to capture the semantics of data, it covers multiple dimensions of
meteorological data including geospatial, temporal, observational, and provenance characteristics.

Based on this semantic model, we built the WeKG-MF knowledge graph considering the open weather
observations published by Météo-France. In 2023, we extended this knowledge graph to include 10 years
(2012-2022) of historical weather observations. In order to enable an interactive visualization of the
WeKG-MF graph, we released a Web application that enables lay users to visualize weather observational
data at different levels of spatio-temporal granularity, and hence, it offers multi-level ’tours’ based on
high-level aggregated views together with on-demand fine-grained data, and this through a unified
multi-visualizations interface [83]).

Therefore, in order to meet specific needs of experts in the agriculture domain and provide them with
access to established and significant climate parameters such as the total monthly amount of precipitation
or the number of days where the amount of precipitation is higher than a threshold over a given period,

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we investigated the use of WeKG-MF in order to generate and calculate a number of significant agro-
meteorological parameters identified in litterature. Thus, based on the WMO recommendations, we
implemented different calculation methods of some of these parameters in SPARQL and stored query
results as part of the WeKG-MF graph. Other agro-meteorological parameters are calculated on the fly. We
devised a strategy to find a balance between calculating on-the-fly agro-meteorological parameters and
pre-calculating and storing significant SPARQL queries results in order to manage the workload of the
WeKG-MF SPARQL endpoint ([83]). We enriched the WeKG-MF graph using an external API of weather
data, namely Open Météo, that provides new agro-meteorological parameters such as solar radiation and
evapotranspiration. And we developed new visualizations in order to allow experts in agriculture to easily
compute, visualize and compare agro-meteorological parameters accross time and space.

On another side, we presented the CoffeeWKG knowledge graph for weather conditions of coffee
regions in Colombia constructed with data extracted from yearbooks based on the WeKG semantic model.
The CoffeeWKG knowledge graph can be downloaded and queried to ask for historical weather conditions
in coffee regions. The main purpose of this graph is intended to correlate this information with coffee crop
data to understand how these conditions may favor or hamper coffee production or diseases (Workshop
Paper 2023).

Independently of these words, we carried out a study [32] on the alignment of two complementary
knowledge graphs useful in agriculture: the thesaurus of cultivated plants in France named French Crop
Usage (FCU) and the French national taxonomic repository TAXREF for fauna, flora, and fungi. Both
knowledge graphs contain vernacular names of plants but those names are ambiguous. Thus, in the
context of the D2KAB project, a group of agricultural experts produced some mappings from FCU crops
to TAXREF. The metadata for the mappings and the mapping set were encoded with the Simple Standard
for Sharing Ontological Mappings (SSSOM), a new model which, among other qualities, offers means
to report on provenance of particular interest for this study. The produced mappings are available for
download in Recherche Data Gouv, the federated national platform for research data in France.

7.3.7 WheatGenomicsSLKG: a Knowledge graph for wheat genomics studies

Participants: Nadia Yacoubi Ayadi, Catherine Faron, Franck Michel, Olivier Corby,

Fabien Gandon.

One of the main challenges in wheat genomics is the large size and complexity of the wheat genome.
Harvesting scientific literature may support experts in wheat genomics to understand hidden interactions
between genomic entities, and the relationships between genotype and phenotypes, based on their
co-occurrence in scientific publications. The main purpose is to bridge the gap between scientific results
presented in publications and experts needs to explore literature and find answers to complex questions.
In the course of 2022, in the context of the D2KAB project, we proposed to structure and integrate
semantic annotations, automatically extracted from scientific articles using NLP tools, into a knowledge
graph. Thus, we rely on the Open Annotation Ontology (OA) to describe, structure and integrate named
entities annotations and their occurrence contexts in texts. Domain specific vocabularies are also reused
to describe bibliographic information of scientific publications. The resulting model was automatically
populated using a mapping-based transformation pipeline implemented with Morph-xR2RML 6.1.21,
and the SPARQL micro-services 6.1.18.

In 2023, we have produced and published an extended release of this kownledge graph (see section
Open data). We have also used the semantic model as well as the pipeline to build two other knowledge
graphs in the context of the D2KAB project, namely RiceGenomicsSLKG and PHB-KG.

The relevance of the semantic model was validated by implementing a set of competency questions
with SPARQL queries which reflect how the KGs can be queried to retrieve co-occurrence of NE in texts.
We also implemented different queries that aim to retrieve annotations from these different graphs
in a federated manner. Our main purpose is to bridge the gap between scientific results presented in
publications and experts needs to explore literature and find answers to complex questions [83]).

7.3.8 Ontology engineering: tooling and methodology

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Participants:

Fabien Gandon, Nicolas Robert.

We contributed to the Agile and Continuous Integration for Modular Ontologies and Vocabularies
(ACIMOV) ontology engineering methodology for developing ontologies and vocabularies. ACIMOV
extends the SAMOD agile methodology to (1) ensure alignment to selected reference ontologies; (2) plan
module development based on dependencies; (3) define ontology modules that can be specialized for
specific domains; (4) empower active collaboration among ontology engineers and domain experts; (5)
enable application developers to select views of the ontology for their specific domain and use case.
ACIMOV adopts the standard git-based approach for coding, leveraging agility and DevOps principles. It
has been designed to be operationalized using collaborative software development platforms such as
Github or Gitlab, and tooled with continuous integration and continuous deployment workflows (CI/CD
workflows) that run syntactic and semantic checks on the repository, specialize modules, generate and
publish the ontology documentation [64].

7.4 Analyzing and Reasoning on Heterogeneous Semantic Graphs

7.4.1 Corese Semantic Web Factory

Participants:

Rémi Ceres, Fabien Gandon, Olivier Corby.

Corese [107], an open-source Semantic Web platform, implements W3C languages such as RDF, RDFS,
OWL RL, SHACL, SPARQL, and extensions including SPARQL Function, SPARQL Transformation, and
SPARQL Rule.

In the enhancement of Corese’s distribution, two new interfaces, Corese-GUI and Corese-Command,
were launched on Flathub. Additionally, a one-click installation script for Corese-Command is now
available for Linux and MacOS.

The documentation of Corese has been fully updated and is accessible at 3.
The new interface, Corese-Command, supplements existing ones such as Corese-Library, Corese-GUI,
Corese-Server, and Corese-Python. Corese-Command, evolving from the previous Corese-CLI, enables
command-line usage of Corese. It encompasses subcommands for converting RDF file formats, running
SPARQL queries, performing SHACL validation on RDF datasets, and executing SPARQL queries on
remote endpoints. Improvements in file loading now allow handling of local files, URLs, or directories.

All interfaces have been unified to support Corese configuration files in properties format.
Enhancements include bug fixes in Corese-Python, addition of Markdown result format for SPARQL,

and N-Quads RDF serialization.

Relevant websites include the Corese project site at Corese Web site and the GitHub repository at

Corese github URL.

7.4.2 SHACL Extension

Participants: Olivier Corby, Iliana Petrova, Fabien Gandon, Catherine Faron.

In the context of a collaboration with Stanford University, we worked on extensions of W3C SHACL

Shape Constraint Language 4.

We conducted a study on large, active, and recognized ontology projects (ex. Gene Ontology, Human
Phenotype Ontology, Mondo Disease Ontology, Ontology for Biomedical Investigations, OBO Foundry,
etc.) as well as an analysis of several existing tools, methodologies and guidelines for ontological engin-
eering.

3Documentation of Corese
4see SHACL Recommendation

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As a result we identified several sets of ontology validation constraints that fall into six big clusters: i)
formalization/modeling checks; ii) terminological/writing checks; iii) documentation/ editorial practices,
terminology-level checks; iv) coherence between terminology and formalization; v) metamodel-based
checks; vi) integration/interoperability/data checking. These can be further refined depending on
whether they are specific to RDFS/OWL meta-model, domain/ontology specific, or Linked Data specific.
This precise categorization of the ontology validation constraints allowed us to analyze the needs and
impact of the extension we are targeting in terms of semantic expressiveness, computational complexity
of the validation, and current syntax of the SHACL language.

We then concentrated on the formalization of the semantic extensions and their validation methods

and came up with a proposal of a corresponding syntactic extensions of SHACL.

The formal specification of the identified extensions enabled us to proceed with the implementation
of a prototype plugin for Protégé (Stanford’s widely used ontology editor) based on the Corese engine and
which extends the SHACL standard with these newly proposed capabilities.

7.4.3 Automatic Assessment of Text Difficulty

Participants: Molka Dhouib, Catherine Faron.

In the continuation of our work in the field of educational resource recommendation, we explored
the possibility of automatically assess text difficulty. For many educational applications, the difficulty
of a text is a key information. Today, the best results for this task are obtained by using NLP and deep
learning techniques. However, the use of these methods can result in the loss of statistical linguistic
information that is important for determining text readability more accurately. In this context, we propose
an approach for assessing text readability by combining neural network models with linguistic features
extracted from the text and integrated into the model to improve the quality of the neural network models.
Experimental results show that this combination outperforms state of the art approaches [87].

7.4.4 On the Automatic Assessment of Natural Language Expert Explanations in Medicine

Participants:

Santiago Marro, Theo Alkibiades Collias, Elena Cabrio, Serena Villata.

The importance of explanations in decision-making, particularly in the medical domain, has been
widely recognized. However, the evaluation of the quality of these explanations remains a challenging
task. In this work, we propose a novel approach for assessing and evaluating the reasons provided in
explanations about clinical cases. Our approach leverages an external knowledge base and a defined
prevalence function to score each reason based on its pertinence in the domain. By applying a determin-
istic prevalence function, we ensure total transparency of the reasons assessment, facilitating a precise
explanation of the rationale behind the scoring hierarchy of each reason. We demonstrate the effect-
iveness of our approach in clinical cases, where medical experts explain the rationale behind a specific
diagnosis and why other potential diagnoses are dismissed. Our methodology provides a nuanced and
detailed evaluation of the explanation, contributing to a more comprehensive understanding of the
decision-making process [68]

7.4.5 Explanatory Argumentation in Natural Language for Correct and Incorrect Medical Diagnoses

Participants:

Benjamin Molinet, Santiago Marro, Elena Cabrio, Serena Villata.

In the context of the ANTIDOTE project, we investigated the generation of natural language argument-
based explanations in medicine. A huge amount of research is carried out nowadays in Artificial Intelli-
gence to propose automated ways to analyze medical data with the aim to support doctors in delivering

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medical diagnoses. However, a main issue of these approaches is the lack of transparency and inter-
pretability of the achieved results, making it hard to employ such methods for educational purposes. It is
therefore necessary to develop new frameworks to enhance explainability in these solutions. we present
a novel full pipeline to generate automatically natural language explanations for medical diagnoses. The
proposed solution starts from a clinical case description associated with a list of correct and incorrect
diagnoses and, through the extraction of the relevant symptoms and findings, enriches the information
contained in the description with verified medical knowledge from an ontology. Finally, the system
returns a pattern-based explanation in natural language which elucidates why the correct (incorrect)
diagnosis is the correct (incorrect) one.

7.4.6 ACTA Module Upgrade

Participants: Cristian Cardellino, Theo Alkibiades Collias.

For the ANTIDOTE project we were in charge of a major refactoring of the original ACTA module 5,
enabling the latest Large Language Models (LLM) models from Hugging Face. The new module is a
standalone Python Library6 available under Apache License. We are also in the process of refactoring
the code for the web application of ACTA to better integrate it with the new modules and writing a code
easier to maintain.

7.4.7 Qualitative evaluation of arguments in persuasive essais

Participants:

Elena Cabrio, Serena Villata, Santiago Marro.

Argumentation is used by people both internally, by evaluating arguments and counterarguments to
make sense of a situation and take a decision, and externally, e.g., in a debate, by exchanging arguments
to reach an agreement or to promote an individual position. In this context, the assessment of the
quality of the arguments is of extreme importance, as it strongly influences the evaluation of the overall
argumentation, impacting on the decision making process. The automatic assessment of the quality of
natural language arguments is recently attracting interest in the Argument Mining field. However, the
issue of automatically assessing the quality of an argumentation largely remains a challenging unsolved
task.

Our contribution is twofold: first, we present a novel resource of 402 student persuasive essays, where
three main quality dimensions (i.e., cogency, rhetoric, and reasonableness) have been annotated, leading
to 1908 arguments tagged with quality facets ; second, we address this novel task of argumentation quality
assessment proposing a novel neural architecture based on graph embeddings, that combines both the
textual features of the natural language arguments and the overall argument graph, i.e., considering also
the support and attack relations holding among the arguments. Results on the persuasive essays dataset
outperform state-of-the-art and standard baselines’ performance. See [68, 69, 100].

7.4.8 RDF Mining

Participants:

Ali Ballout, Catherine Faron, Rémi Felin, Andrea Tettamanzi.

The Shapes Constraint Language (SHACL) is a W3C recommendation which allows to represent
constraints in RDF, and validate RDF data graphs against these constraints. A SHACL validator produces
a validation report whose result is false for a shape graph as soon as there is at least one node in the

5Acta module web site
6Acta library on Github

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RDF data graph that does not conform to the shape. This Boolean result of the validation of an RDF
data graph against an RDF shape graph is not suitable for discovering new high-potential shapes from
the RDF data. Therefore, we have proposed a probabilistic framework to accept shapes with a realistic
proportion of nodes in an RDF data graph that does not conform to it. Based on this framework, we
have also proposed an extension of the SHACL validation report to express a set of metrics including the
generality and likelihood of shapes and we define a method to test a shape as a hypothesis test [61, 60].
The task of evaluating the fitness of a candidate axiom against known facts or data is known as
candidate axiom scoring. Being able to accurately score candidate axioms is a prerequisite for automatic
schema or ontology induction, but can also be useful for ontology and/or knowledge graph validation.
Accurate axiom scoring heuristics are often heavy to compute, which is a big problem if one wants to
exploit them in iterative search methods like level-wise generate-and-test or evolutionary algorithms,
where large numbers of candidate axioms need to be scored. We have tackled the challenge of learning a
predictive model as a surrogate to reasoning, that predicts the acceptability of candidate class axioms,
that is fast to execute yet accurate enough to be used in such settings. For this purpose, we have leveraged
a semantic similarity measure extracted from the subsumption hierarchy of an ontology and we have
proven that our proposed method is able to learn the acceptability labels of candidate OWL class axioms
with high accuracy and that it can do so for multiple types of OWL class axioms [53].

7.4.9 Capturing Geospatial Knowledge from Real-Estate Advertisements

Participants:

Lucie Cadorel, Andrea Tettamanzi.

In the framework of a CIFRE thesis with Septeo Proptech, we have proposed a workflow to extract
geographic and spatial entities based on a BiLSTM-CRF architecture (Bidirectional Long Short-Term
Memory Conditional Random Field) with a concatenation of several text representations and to extract
spatial relations, to build a structured Geospatial knowledge base. This pipeline has been applied to the
case of French housing advertisements, which, generally provide information about a property’s location
and neighbourhood. Our results show that the workflow tackles French language and the variability and
irregularity of housing advertisements, generalizes Geoparsing to all geographic and spatial terms, and
successfully retrieves most of the relationships between entities from the text [86].

Text representations are widely used in NLP tasks such as text classification. Very powerful models
have emerged and been trained on huge corpora for different languages. However, most of the pre-
trained models are domain-agnostic and fail on domain-specific data. We performed a comparison of
different text representations applied to French Real Estate classified advertisements through several
text classification tasks to retrieve some key attributes of a property. Our results demonstrate the limit-
ations of pre-trained models on domain-specific data and small corpora, but also the strength of text
representation, in general, to capture underlying knowledge about language and stylistic specificities [58].

7.4.10 ISSA: semantic indexing of scientific articles and advanced services

Participants:

Anna Bobasheva, Catherine Faron, Aline Menin, Franck Michel,
Marco Winckler.

As a continuation of the ISSA project that ended in 2022, in 2023 we started the ISSA 2 project. In
line with open science and the FAIR principles, ISSA 2 proposes a generic method to explore the data
available in an open archive in order to 1) extract new knowledge, 2) exploit this knowledge with a
bibliometric objective and 3) propose services to researchers and documentalists, in particular in terms
of bibliometrics and information retrieval. It relies on and extends the outcomes of the ISSA project, in
particular the semantic index of the publications of an open archive (metadata, descriptors and named
entities mentioned in the text, linked to standard knowledge bases). The proposed methods exploit data
mining techniques as well as techniques for the construction, publication and exploitation of knowledge

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Inria Annual Report 2023

graphs. In addition to Agritrop, CIRAD’s open archive that served as a use case in ISSA, ISSA 2 considers
the HAL instance of the UR EuroMov Digital Health in Motion.

In 2023, we produced a new version of the indexing pipeline (6.1.14) that extends the scope of data
represented in RDF, supports multi-instance pipelines, and facilitates the deployment process. It has
been set in production and now performs the weekly indexing of both archives: Agritrop and HAL
EuroMov Digital Health in Motion. We also developed a new search interface capable of exploiting the
rich semantics of reference vocabularies to provide more intelligent search results than simple keyword-
base searches. Finally, we developed a novel visualization technique based on polymorphic glyphs to
support the visual exploration of hierarchical spatio-temporal data [70].

The pipeline and search interface are DOI-identified and available under an open license on public
repositories. The knowledge graph produced by the pipeline for the Agritrop archive is also made public
as a downloadable dump (DOI: 10.5281/zenodo.10381606) and through a public SPARQL endpoint. We
published new results in a journal article: [44].

7.4.11 IndeGx: A Model and a Framework for Indexing Linked Datasets and their Knowledge Graphs

with SPARQL-based Test Suits

Participants:

Pierre Maillot, Olivier Corby, Catherine Faron, Fabien Gandon,
Franck Michel.

The joint exploitation of RDF datasets relies on the knowledge of their content, of their endpoints, and
of what they have in common. Yet, not every dataset contains a self-description, and not every endpoint
can handle the complex queries used to generate such a description.

As part of the ANR DeKaloG, we proposed a standard-based approach to generate the description
of a dataset. The description generated as well as the process of its computation are expressed using
standard vocabularies and languages. We have implemented our approach into a framework, called
IndeGx, to automatically generate the description of datasets and endpoints and collect them in an index.
We have experimented IndeGx on a set of 339 active knowledge bases. The method and the results of our
experimentation have been published in a journal article: [38].

Several visualizations were also generated from IndeGx and are available online: IndeGx Web Site.

7.4.12 Metadatamatic: A tool for the description of RDF knowledge bases

Participants:

Pierre Maillot, Olivier Corby, Catherine Faron, Fabien Gandon,
Franck Michel.

During the experimentations done as part of the development of IndeGx, we observed that less than
10% of accessible RDF datasets offer a description of their provenance. We theorize that this is partly due
to the difficulty for data providers to learn how to create such descriptions. Some initiatives such as the
COST KG working group, or a European Commission action have proposed guides consisting of lists of
mandatory and recommended classes and properties to use.

We propose Metadatamatic, an online tool based on the before-mentioned initiatives to generate a
KB description. Metadatamatic aims to bypass this learning problem and help further the description
of a KB. Metadatamatic generates a description using well-established vocabulary from a simple Web
form. It also offers to extract some parts of the description, such as the list of vocabularies used in the
data, automatically from the content of the KB. We hope that this tool will lead to an improvment of the
usability of Linked Data in general.

Metadatamatic was presented as a conference poster ([67]) and is available online: Metadatamatic

7.4.13 Learning Pattern-Based Extractors from Natural Language and Knowledge Graphs: Applying

Large Language Models to Wikipedia and Linked Open Data

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Participants: Célian Ringwald, Fabien Gandon, Catherine Faron, Franck Michel,

Hanna Abi Akl.

Whether automatically extracted from structured elements of articles or manually populated, the
open and linked data published in DBpedia, and Wikidata offer rich and structured complementary
views of the textual descriptions found in Wikipedia. However, the unstructured text of Wikipedia articles
contains a lot of information that is still missing in DBpedia and Wikidata. Extracting them would be
interesting to improve the coverage and quality of these knowledge graphs (KG) and this would have an
important impact on all downstream tasks.

This work proposes to exploit the dual bases formed from Wikipedia pages and Linked Open Data
(LOD) bases covering the same subjects in natural language and in RDF, to produce RDF extractors
targeting specific RDF patterns and tuned for a given language. Therefore, the main research question is:
Can we learn efficient customized extractors targeting specific RDF patterns from the dual base formed
by Wikipedia on one hand, and DBpedia and Wikidata on the other hand?

The landscape of the research field drawn at the intersection of language models and knowledge
graphs is very dynamic and quickly evolving. For this reason, as the first step of this work, we designed an
extended systematic review of the latest NLP approaches to KG extraction.

In a second step we started to the design a first dataset focused on datatype properties. We restricted
the selection of our training to facts respecting a given SHACL shape and information that could be
found in the Wikipedia abstract. Then to learn how to extract relations with datatype properties from
natural language, we exploited pre-trained encoder-decoder models, and more precisely BART (denoising
autoencoder sequence-to-sequence model). We explored several aspects of the task formulation that
could impact the generation of triples in this context: the size of the model, the size of the learning sample
needed to learn a given SHACL pattern, and the syntax of the triples. The current work will be published
in two papers accepted at AAAI 2024 (Association for the Advancement of Artificial Intelligence).

Alongside the pursuit of the main objectives, the work started by a participation to the International
Semantic Web Research Summer School and the Generative Modeling Summer School where was it was
presented [104]. ISWS was the opportunity to collaborate and work on a question related to generative IA,
and more especially on how to render triples describing fictional characters as illustratve images [50].
This collaboration led us to attend at the King’s College Knowledge prompting hackathon where we had
the occasion to develop a first proposal related to visual relation extraction.

7.4.14 AI agent to convert natural language questions into SPARQL queries

Participants:

Emma Tysinger, Fabien Gandon.

An experimental knowledge graph (KG) driven framework (10.26434/chemrxiv-2023-sljbt) was re-
cently introduced to facilitate the integration of heterogeneous data types, encompassing both exper-
imental data (mass spectrometry annotation, results from biological screening and fractionation) as
well as meta-data available on the Web (such as taxonomies and metabolite databases). Although this
KG efficiently encapsulates the different data structures and semantic relationships, retrieving specific
information through structured or visually queries or even programmatically, is not trivial. In a collab-
orative project KG-Bot we designed and implemented an AI agent that can convert natural language
questions into SPARQL queries and programmatic data-mining tasks and generate adapted visualization.
By leveraging the potential of emerging Large Language Models (LLMs) to understand semantic relation-
ships encapsulated in KGs and mentioned in the questions, the agent autonomously iterates to construct
a SPARQL query of any submitted natural language question. After retrieving the necessary information
from the KG, the agent provides a preliminary interpretation of the results in natural language, along with
relevant visualizations and statistics [82].

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Inria Annual Report 2023

7.4.15 Hybridizing machine learning and knowledge graphs: vision, model evaluation, and know-

ledge injection

Participants:

Pierre Monnin.

Knowledge graphs (KGs) are nowadays largely adopted, representing a successful paradigm of how
symbolic and transparent AI can scale on the World Wide Web. However, they are generally tackled by
Machine Learning (ML) and mostly numeric based methods such as graph embedding models (KGEMs)
and deep neural networks (DNNs). The latter methods have been proved efficient but lack some major
characteristics such as interpretability and explainability. Conversely, these characteristics are intrinsically
supported by symbolic AI methods and artefacts, thus motivating a research effort to hybridize machine
learning and knowledge graphs.

In this direction, in a vision paper, we introduced some of the main existing methods for combining
KGs and ML and highlighted research gaps and perspectives that we deem promising and currently under-
explored for the involved research communities [36]. These span from KG support for prompting Large
Language Models, integration of KG semantics in ML models to symbol-based methods, interpretability
of ML models, and the need for improved benchmark datasets.

We investigated the evaluation of KGEMs for the task of link prediction which aims at predicting the
missing tail of a triple (h, r, ?) or the missing head of a triple (?, r, t ). Usually, their performance is assessed
using rank-based metrics such as Hits@K , MR, or MRR, which evaluate their ability to give high scores to
ground-truth entities. We extended this evaluation framework with a semantic perspective by proposing
the metric Sem@K that measures the capability of models to predict valid entities w.r.t. domain and
range constraints [37]. We designed different versions of Sem@K depending on the presence or not of
types, potentially hierarchically organized. We also evaluated a wide array of KGEMs which showed that
Sem@K offers an additional perspective beside rank-based metrics, and that some families of models
perform better than others.

We proposed one possible way to inject knowledge represented as RDFS constructs into KGEMs
to learn embeddings that better capture semantics [65]. More specifically, we introduced protographs
that are built from relation signatures (i.e., domain and range) and the class hierarchy. Embeddings are
learned on such protographs and are supposed to encapsulate the semantics of the KGs. Protograph-
based embeddings are then used to bootstrap embeddings learned on the actual KG, yielding better
performance in downstream tasks (link prediction, entity clustering, and node classification). These
results highlight the interest of injecting knowledge into KGEMs, a current and vibrant direction of the
research community.

7.4.16 Semantic Overlay Network for Linked Data Access

Participants:

Fabien Gandon, Mahamadou Toure.

We proposed and evaluated MoRAI (Mobile Read Access in Intermittent internet connectivity), a
distributed peer-to-peer architecture organized in three levels dedicated to RDF data exchanges by
mobile contributors. We presented the conceptual and technical aspects of this architecture as well as
a theoretical analysis of the different characteristics. We then evaluated it experimentally and results
show the relevance of considering geographical positions during data exchanges and of integrating RDF
graph replication to ensure data availability in terms of requests completion rate and resistance to crash
scenarios [101].

8 Bilateral contracts and grants with industry

8.1 Bilateral contracts with industry

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Curiosity Collaborative Project

Participants: Catherine Faron, Oscar Rodríguez Rocha, Molka Dhouib.

Partner: TeachOnMars.
This collaborative project with the TeachOnMars company started in October 2019. TeachOnMars
is developing a platform for mobile learning. The aim of this project is to develop an approach for
automatically indexing and semantically annotating heterogeneous pedagogical resources from different
sources to build up a knowledge graph enabling to compute training paths, that correspond to the
learner’s needs and learning objectives.

CIFRE Contract with Doriane

Participants:

Andrea Tettamanzi, Rony-Dupuy Charles.

Partner: Doriane.
This collaborative contract for the supervision of a CIFRE doctoral scholarship, relevant to the PhD
of Rony-Dupuy Charles, is part of Doriane’s Fluidity Project (Generalized Experiment Management),
the feasibility phase of which has been approved by the Terralia cluster and financed by the Région
Sud-Provence Alpes Côte d’Azur and BPI France in March 2019. The objective of the thesis is to develop
machine learning methods for the field of agro-vegetation-environment. To do so, this research work
will take into account and address the specificities of the problem, i.e. data with mainly numerical
characteristics, scalability of the study object, small data, availability of codified background knowledge,
need to take into account the economic stakes of decisions, etc. To enable the exploitation of ontological
resources, the combination of symbolic and connective approaches will be studied, among others. Such
resources can be used, on the one hand, to enrich the available datasets and, on the other hand, to restrict
the search space of predictive models and better target learning methods.

The PhD student will develop original methods for the integration of background knowledge in the
process of building predictive models and for the explicit consideration of uncertainty in the field of
agro-plant environment.

CIFRE Contract with Kinaxia

Participants:

Andrea Tettamanzi, Lucie Cadorel.

Partner: Kinaxia.
This thesis project is part of a collaboration with Kinaxia that began in 2017 with the Incertimmo
project. The main theme of this project was the consideration of uncertainty for a spatial modeling of
real estate values in the city. It involved the computer scientists of the Laboratory and the geographers
of the ESPACE Laboratory. It allowed the development of an innovative methodological protocol to
create a mapping of real estate values in the city, integrating fine-grained spatiality (the street section), a
rigorous treatment of the uncertainty of knowledge, and the fusion of multi-source (with varying degrees
of reliability) and multi-scale (parcel, street, neighbourhood) data.

This protocol was applied to the Nice-Côte d’Azur metropolitan area case study, serving as a test bed

for application to other metropolitan areas.

The objective of this thesis, which was carried out by Lucie Cadorel under the supervision of Andrea
Tettamanzi, was, on the one hand, to study and adapt the application of methods for extracting knowledge
from texts (or text mining) to the specific case of real estate ads written in French, before extending them
to other languages, and, on the other hand, to develop a methodological framework that makes it possible
to detect, explicitly qualify, quantify and, if possible, reduce the uncertainty of the extracted information,
in order to make it possible to use it in a processing chain that is finalized for recommendation or decision
making, while guaranteeing the reliability of the results.

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Inria Annual Report 2023

Plan de Relance with Startin’Blox

Participants:

Pierre-Antoine Champin, Fabien Gandon, Maxime Lecoq.

Partner: Startin’Blox.
The subject of this project is to investigate possible solutions to build on top of the Solid architecture
capabilities to discover services and access distributed datasets. This would rely on standardized search
and filtering capabilities for the SOLID PODs, as well as on traversal or federated SPARQL query solving
approaches to design a pilot architecture. We also intend to address performance issues via caching or
indexing strategies in order to allow a deployment of the Solid ecosystem on a web scale.

CP4SC Project - sub contract

Participants:

Fabien Gandon, Rémi Ceres.

Partner: Atos.
Initiated in January 2023, the CP4SC project is a collaborative effort with Atos that focuses on devel-
oping a Cloud Platform for Smart Cities. This innovative platform is designed to integrate and analyze
data from a wide range of city services and smart devices. As a single, secure access point for data
management and dissemination, it promotes innovation and interdepartmental cooperation in urban
areas. The project targets sustainable urban development by addressing three key areas: optimizing
resource consumption in various living environments through energy management; reducing green-
house gas emissions via mobility management and alternative transportation solutions; and enhancing
environmental conservation and public health through strategic management and observation of the
Earth’s environment. In that context Atos has a contract with Wimmics to study the use of semantic
technologies and CORESE in managing the data of this Smart City scenario.

9 Partnerships and cooperations

9.1 International initiatives

9.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria

International Program

PROTEMICS

Participants: Olivier Corby, Catherine Faron, Fabien Gandon, Iliana Petrova.

Title: Protégé and SHACL extension to support ontology validation

Duration: 2020 -> 2023

Coordinator: Rafael Gonçalves (rafael.goncalves@stanford.edu)

Partners:

• Stanford University Stanford (USA)

Inria contact: Fabien Gandon

Project WIMMICS

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Summary: We propose to investigate the extension of the structure-oriented SHACL validation to in-
clude more semantics, and to support ontology validation and the modularity and reusability of
the associated constraints. Where classical Logical (OWL) schema validation focuses on checking
the semantic coherence of the ontology, we propose to explore a language to capture ontology
design patterns as extended SHACL shapes organized in modular libraries. The overall objective
of our work is to augment the Protégé editor with fundamental querying and reasoning capabil-
ities provided by CORESE, in order to assist ontology developers in performing ontology quality
assurance throughout the life-cycle of their ontologies

9.1.2 Participation in other International Programs

MERI-Wimmics collaboration

Participants:

Antoine Vidal-Mazuy, Michel Buffa.

Visited institution: MERI team at CCRMA/Stanford

Country: USA

Dates: 4-22 May, 2023

Context of the visit: scientific collaboration on Web Audio Modules.

Description: Research stay and lectures.

9.2 International research visitors

9.2.1 Visits of international scientists

Other international visits to the team

Matteo Palmonari

Status Associate Professor

Institution of origin: University of Milano Bicocca

Country: Italy

Dates: from 26-06-23 to 25-07-23

Context of the visit: Research stay to investigate the application of LLMs to the argument mining task.

Mobility program/type of mobility: Research stay funded by 3IA Côte d’Azur

Cristhian Figueroa

Status Assistant Professor

Institution of origin: University del Cauca

Country: Colombia

Dates: from 23-06-23 to 07-07-23

Context of the visit: Research stay to investigate the construction of a knowledge graph from agro-

meteorological data related to cafe growing in Colombia.

Mobility program/type of mobility: Research stay funded by University del Cauca

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Inria Annual Report 2023

9.2.2 Visits to international teams

Research stays abroad

Marco Winckler

Visited institution: University of Bari

Country: Italy

Dates: from 23-10-23 to 19-11-23

Context of the visit: Invited Professor by the University of Bari

Mobility program/type of mobility: research stay/lecture

9.3 European initiatives

9.3.1 H2020 projects

AI4Media

Title: AI4Media

Duration: 2020 - 2024

Coordinator: The Centre for Research and Technology Hellas (CERTH)

Partners:

see consortium

Inria contact: through 3IA

Summary: AI4Media is a 4-year-long project. Funded under the European Union’s Horizon 2020 research
and innovation programme, the project aspires to become a Centre of Excellence engaging a wide
network of researchers across Europe and beyond, focusing on delivering the next generation of
core AI advances and training to serve the Media sector, while ensuring that the European values
of ethical and trustworthy AI are embedded in future AI deployments. AI4Media is composed of
30 leading partners in the areas of AI and media (9 Universities, 9 Research Centres, 12 industrial
organizations) and a large pool of associate members, that will establish the networking infrastruc-
ture to bring together the currently fragmented European AI landscape in the field of media, and
foster deeper and long-running interactions between academia and industry.

9.3.2 Other european programs/initiatives

HyperAgents - SNSF/ANR project

Title: HyperAgents

Duration: 2020 - 2024

Coordinator: Olivier Boissier, MINES Saint-Étienne

Partners:

• MINES Saint-Étienne (FR)

• INRIA (FR)

• Univ. of St. Gallen (HSG, Switzerland)

Inria contact: Fabien Gandon

Project WIMMICS

47

Summary: The HyperAgents project, Hypermedia Communities of People and Autonomous Agents,
aims to enable the deployment of world-wide hybrid communities of people and autonomous
agents on the Web. For this purpose, HyperAgents defines a new class of multi-agent systems that
use hypermedia as a general mechanism for uniform interaction. To undertake this investigation,
the project consortium brings together internationally recognized researchers actively contributing
to research on autonomous agents and MAS, the Web architecture, Semantic Web, and to the
standardization of the Web. Project Web site: HyperAgents Project

ANTIDOTE - CHIST-ERA project

Title: ANTIDOTE

Duration: 2020 - 2024

Coordinator: Elena Cabrio, Serena Villata

Partners:

• University of the Côte d’Azur (Wimmics Team)

• Fondazione Bruno Kessler (IT)

• University of the Basque Country (ES)

• University of Leuven (Belgium)

• University of Lisbon (PT)

Summary: Providing high quality explanations for AI predictions based on machine learning requires to
combine several interrelated aspects, including, among others: selecting a proper level of general-
ity/specificity of the explanation, considering assumptions about the familiarity of the explanation
beneficiary with the AI task under consideration, referring to specific elements that have contrib-
uted to the decision, making use of additional knowledge (e.g. metadata) which might not be
part of the prediction process, selecting appropriate examples, providing evidences supporting
negative hypothesis, and the capacity to formulate the explanation in a clearly interpretable, and
possibly convincing way. According to the above considerations, ANTIDOTE fosters an integrated
vision of explainable AI, where low level characteristics of the deep learning process are combined
with higher level schemas proper of the human argumentation capacity. ANTIDOTE will exploit
cross-disciplinary competences in three areas, i.e. deep learning, argumentation and interactivity,
to support a broader and innovative view of explainable AI. Although we envision a general integ-
rated approach to explainable AI, we will focus on a number of deep learning tasks in the medical
domain, where the need for high quality explanations, both to clinicians and to patients, is perhaps
more critical than in other domains. Project Web site: Antidote Project

9.4 National initiatives
ANR D2KAB

Participants: Olivier Corby, Catherine Faron, Fabien Gandon, Franck Michel, Na-

dia Yacoubi Ayadi.

Partners: LIRMM, INRAE, IRD, ACTA
D2KAB is an ANR project which started in June 2019 until June 2024, led by the LIRMM laboratory
(UMR 5506). Its general objective is to create a framework to turn agronomy and biodiversity data into
knowledge –semantically described, interoperable, actionable, open– and investigate scientific methods
and tools to exploit this knowledge for applications in science and agriculture. Within this project the
Wimmics team is contributing to the lifting of heterogeneous dataset related to agronomy coming from
the different partners of the project and is responsible to develop a unique entry point with semantic
querying and navigation services providing a unified view on the lifted data.

Web site: D2KAB Project

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ANR DeKaloG

Inria Annual Report 2023

Participants: Olivier Corby, Catherine Faron, Fabien Gandon, Pierre Maillot,

Franck Michel.

Partners: Université Nantes, INSA Lyon, Inria Center at Université Côte d’Azur
DeKaloG (Decentralized Knowledge Graphs) is an ANR project until June 2024 that aims to: (1)
propose a model to provide fair access policies to KGs without quota while ensuring complete answers to
any query. Such property is crucial for enabling web automation, i.e. to allow agents or bots to interact
with KGs. Preliminary results on web preemption open such perspective, but scalability issues remain; (2)
propose models for capturing different levels of transparency, a method to query them efficiently, and
especially, techniques to enable web automation of transparency. (3) propose a sustainable index for
achieving the findability principle.

DeKaloG Web site

ANR ATTENTION

Participants:

Serena Villata, Elena Cabrio, Xiaoou Wang, Pierpaolo Goffredo.

The ANR project ATTENTION started in January 2022 with Université Paris 1 Sorbonne, CNRS (Centre
Maurice Halbwachs), EURECOM, Buster.Ai. The coordinator of the project is CNRS (Laboratoire I3S) in
the person of Serena Villata.

In the ATTENTION project, we propose to address the urgent need of designing intelligent semi-
automated ways to generate counter-arguments to fight the spread of disinformation online. The idea is
to avoid the undesired effects that come with content moderation, such as overblocking , when dealing
with disinformation online, and to directly intervene in the discussion (e.g., Twitter threads) with textual
arguments that are meant to counter the fake content as soon as possible, and prevent it from further
spreading. A counter-argument is a non- aggressive response that offers feedback through fact-bound
arguments, and can be considered as the most effective approach to withstand disinformation. Our
approach aims at obtaining high quality counter-arguments while reducing efforts and supporting human
fact-checkers in their everyday activities.

ANR CIGAIA

Participants:

Serena Villata, Elena Cabrio, Pierpaolo Goffredo.

The ANR ASTRID CIGAIA project (December 2022 - 30 Months) "Controversy and influence in the
Ukraine war: a study of argumentation and counter-argumentation through Artificial Intelligence" aims
to bring together, in a multi-disciplinary way, the research fields of discourse analysis in the humanities
and social sciences, with those of automatic extraction of natural language arguments from text (argument
mining) in Artificial Intelligence.

Partners are Ecole de l’Air et de l’Espace (EAE), which is also the coordinator of the project, and CNRS

(Laboratoire I3S).

ANR CROQUIS

Participants:

Andrea Tettamanzi.

The ANR project CROQUIS (March 2022 - 48 months) with CRIL (Lens) and HSM (Montpellier). The
coordinator of the project is Salem Benferhat (CRIL). The local coordinator for Laboratoire I3S is Andrea

Project WIMMICS

49

Tettamanzi. The local unit involves two other members of I3S which are not part of WIMMICS, namely
Célia da Costa Pereira and Claude Pasquier. The contribution of Wimmics is focused on addressing the
problem of incomplete and uncertain data.

Web site: CROQUIS Project

ANR AT2TA

Participants:

Pierre Monnin.

Partners: Université de Lorraine (LORIA), Inria Paris (HeKA team), Université Paul Sabatier (IRIT),

IHU Imagine, Université Côte d’Azur (I3S), Infologic

The ANR project AT2TA started in February 2023 until February 2026. The coordinator of the project
is Miguel Couceiro (LORIA, Université de Lorraine). The local coordinator for I3S / Wimmics is Pierre
Monnin. The project aims to develop an analogy-based machine learning framework and to demonstrate
its usefulness in real case scenarios. Within the project, the Wimmics team is contributing by investigating
the potential usages of analogy-based framewoks with and for knowledge graphs, and the associated
adequat representation spaces.
Web site: AT2TA project

ISSA (AAP Collex-Persée)

Participants:

Franck Michel, Anna Bobasheva, Olivier Corby, Catherine Faron,
Aline Mennin, Marco Winckler.

Partners: CIRAD, Mines d’Alès
The ISSA project started in October 2020 and is led by the CIRAD. It aims to set up a framework for the
semantic indexing of scientific publications with thematic and geographic keywords from terminological
resources. It also intends to demonstrate the interest of this approach by developing innovative search
and visualization services capable of exploiting this semantic index. Agritrop, Cirad’s open publications
archive, serves as a use case and proof of concept throughout the project. In this context, the primarily
semantic resources are the Agrovoc thesaurus, Wikidata and GeoNames.

Wimmics team is responsible for (1) the generation and publication of the knowledge graph represent-
ing the indexed entities, and (2) the development of search/visualization tools intended for researchers
and/or information

CROBORA: Crossing Borders Archives: understanding the circulation of images of Europe (ANR)

Participants: Marco Winckler, Aline Menin.

Coordinator: Matteo Treleani, SicLab, Université Côte d’Azur
The CROBORA project (ANR-20-CE38-0002) project led by the Sic.Lab laboratory at EUR CREATES,
University of Côte d’Azur, funded by ANR from 2021 to 2024. CROBORA studies the circulation of archive
images in the media space. The main hypothesis of the project is that what determines the circulation
of archives thus constituting the visual memory of the European construction is not a decision that lies
solely in the hands of the authors of the reuses (journalists, audiovisual professionals) but rather the
consequence of a series of mediations that can be technical (the availability of archives for example),
interprofessional (the relationship between archival institutions and the media), cultural (the use of
a document for a purpose in one country or another), historical (an archive sequence can change its
meaning over time) etc. The general objective of the project is therefore to understand the logics governing
the circulation of audiovisual archives. The project aims to respond to the following sub-objectives: 1.
to understand which audiovisual fragments are reused in the media to talk about Europe; 2. to build a

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Inria Annual Report 2023

cartography of frequently used symbolic images; 3. to analyze the representations carried by these images;
4. to understand their trajectory in order to see how they are reshaped diachronically and according to
different media, countries and institutions; and finally 5. to identify which mediations determine their
readjustments.

Wimmics team is responsible for (1) the development of visualization tools for supporting the explor-
ation of the CROBORA dataset, and (2) investigating algorithms for optimizing the visual search in the
dataset.

9.5 Regional initiatives

3IA Côte d’Azur

Participants:

Elena Cabrio, Catherine Faron, Fabien Gandon, Freddy Limpens, An-
drea Tettamanzi, Serena Villata.

3IA Côte d’Azur is one of the four “Interdisciplinary Institutes of Artificial Intelligence”7 that were created
in France in 2019. Its ambition is to create an innovative ecosystem that is influential at the local, national,
and international level. The 3IA Côte d’Azur institute is led by Université Côte d’Azur in partnership with
major higher education and research partners in the region of Nice and Sophia Antipolis: CNRS, Inria,
INSERM, EURECOM, ParisTech MINES, and SKEMA Business School. The 3IA Côte d’Azur institute is
also supported by ECA, Nice University Hospital Center (CHU Nice), CSTB, CNES, Data Science Tech
Institute and INRAE. The project has also secured the support of more than 62 companies and start-ups.
We have four 3IA chairs for tenured researchers of Wimmics and several grants for PhD and postdocs.
We also have an industrial 3IA Affiliate Chair with the company Mnemotix focused on the industrial-

isation and scalability of the CORESE software.

Automazoo

Participants:

Arnaud Barbe, Molka Dhouib, Catherine Faron.

Coordinators: Catherine Faron and Marco Corneli (CEPAM, UCA)
Automazoo was a 1-year project funded by the Académie d’Excellence “Homme, Idées et Milieux”
of Université Côte d’Aur. Its overall aim was to adopt, adapt and combine methods from automatic
language processing, knowledge representation and reasoning, and machine learning to analyze, classify
and automate the semantic annotation of ancient texts.

HISINUM

Participants:

Arnaud Barbe, Molka Dhouib, Catherine Faron, Andrea Tettamanzi.

Coordinators: Muriel Dal Pont Legrand (GREDEG), Mélanie Plouviez (CRHI), Catherine Faron (I3S)

and Arnaud Zucker (CEPAM)

HISINUM is a 3-year project funded by the Académie d’Excellence “Homme, Idées et Milieux” of
Université Côte d’Aur. The aim of this project is to reflect on how digital humanities are renewing research
practices and the issue of data in the humanities and social sciences, and on the epistemological impact
of the new tools and their ability to change disciplinary boundaries.

73IA Côte d’Azur Web site

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10 Dissemination

10.1 Promoting scientific activities

10.1.1 Scientific events: organisation

General chair, scientific chair

• Catherine Faron: Co-chair of the 1st Workshop on Controlled Vocabularies and Data Platforms
for Smart Food Systems (SmartFood 2023), colocated with ER 2023, November 6, 2023, Lisbon,
Portugal.

• Pierre Monnin: co-general chair of the Neuro-symbolic AI workshop (supported by GT MHyIA of
GDR RADIA and AfIA, and co-located with ECSQARU 2023), September 19, 2023, Arras, France.

• Andrea Tettamanzi: co-chair of the 1st Workshop on AI-driven heterogeneous data management:
Completing, merging, handling inconsistencies and query-answering (ENIGMA 2023), Co-located
with KR 2023, September 3-4, 2023, Rhodes, Greece

Member of the organizing committees

• Elena Cabrio and Serena Villata: Local organizers of the “Natural Language Argumentation” event

(GDR TAL and GDR RADIA), November 27th, 2023 in Sophia Antipolis.

• Aline Menin: PhD Day at the Extended Reality Research and Creative Center (XR2C2), July 7, 2023,

Cannes, France.

10.1.2 Scientific events: selection

Chair of conference program committees

• Elena Cabrio: Co-chair of Diversity and Inclusion, The 17th Conference of the European Association

of Computational Linguistics (EACL 2023).

• Catherine Faron: Program chair of the French Conference on Knowledge Extraction and Manage-

ment (EGC 2023), January 16-20, 2023, Lyon, France.

• Serena Villata: Co-chair of Tutorial and Workshop, 20th International Conference on Principles of

Knowledge Representation and Reasoning, KR 2023 September 2-8, 2023, Rhodes, Greece

• Marco Winckler:

– Technical Program Chair of the 15th The ACM SIGCHI Symposium on Engineering Interactive

Computing Systems (EICS’2023), June 27-30, 2023, Swansea, UK.

– Technical Program Chair of the INTERACT 2023 - IFIP TC 13 International Conference on
Human Computer Interaction, August 28th - September 1st 2023, University of York, UK.

Member of the conference program committees

• Elena Cabrio: Area Chair of: the 2023 Conference on Empirical Methods in Natural Language
Processing (EMNLP), the 61st Annual Meeting of the Association for Computational Linguistics
(ACL), ACL ARR Rolling Reviews; Member of the Senior Program Committee of: International Joint
Conference on Artificial Intelligence (IJCAI), European Conference on Artificial Intelligence (ECAI),
Association for the Advancement of Artificial Intelligence (AAAI) .

• Molka Dhouib: Ingénierie de connaissances, European Semantic Web Conference

• Catherine Faron: Senior member of Program Comittees of International Semantic Web Conference
(ISWC) 2023 and European Semantic Web Conference (ISWC) 2023 (resource track), member
of Program Committees of European Semantic Web Conference (ISWC) 2023 (research track),
Semantics 2023, FOIS 2023, ER 2023, SWODCH 2023

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Inria Annual Report 2023

• Fabien Gandon: member of Program Committees of European Conference on Artificial Intelligence
(ECAI) 2023, European Semantic Web Conference (ISWC) 2023, International Joint Conference
on Artificial Intelligence (IJCAI) 2023, International Semantic Web Conference (ISWC) 2023 and
TheWebConf 2023

• Alain Giboin: Member of the scientific committee of the MODACT 2023 conference (11-13 May

2023, Paris) on the modeling of human activity

• Pierre Monnin: member of Program Committees of International Semantic Web Conference (ISWC)

2023, ECML-PKDD 2023, TheWebConf 2024

• Aline Menin: IEEE International Conference on Virtual Reality and 3D User Interfaces, Eurographics
Conference on Visualization, ACM SIGCHI Symposium on Engineering Interactive Computing
Systems

• Franck Michel: member of Program Committees of Semantics 2023, TheWebConf 2024

• Andrea Tettamanzi: GECCO, ICAAWI-IAT’23.

• Serena Villata: Area Chair of: the 2023 Conference on Empirical Methods in Natural Language Pro-
cessing (EMNLP), the 61st Annual Meeting of the Association for Computational Linguistics (ACL),
ACL ARR Rolling Reviews; Area chair of: International Joint Conference on Artificial Intelligence
(IJCAI), European Conference on Artificial Intelligence (ECAI), Association for the Advancement of
Artificial Intelligence (AAAI) .

• Marco Winckler: member of the Program Committes of INTERACT 2023, ACM EICS 2023, EuroVis

2023, ICWE 2023, IS-EUD 2023, ACM IUI 2023, WISE 2023, SVR 2023.

10.1.3 Journal

Member of the editorial boards

• Elena Cabrio: Italian Journal of Computational Linguistics (IJCoL ISSN 2499-4553).

• Catherine Faron: Transactions On Graph Data and Knowledge (TGDK), Data and Knowledge

Engineering (DKE), Revue Ouverte d’Intelligence Artificielle (ROIA)

• Pierre Monnin: Transactions On Graph Data and Knowledge (TGDK)

• Célian Ringwald: Editor at Programming Historian.

• Serena Villata: Artificial Intelligence and Law, Argument and Computation and Journal of Web

Semantics

• Marco Winckler: Journal of Web Engineering (River Publishers), Interacting with Computers (Ox-
ford Press), Behaviour and Information Technology (Tailor and Francis), PACM Proceedings on
Human-Computer Interaction (ACM Sheridam), IFIP Advances in Information and Communication
Technology (Springer).

Reviewer - reviewing activities

• Catherine Faron: Journal of Web Semantics (JOWS), ACM Journal on Computing and Cultural

Heritage (JOCCH)

• Fabien Gandon: ACM Transactions on the Web, Springer Heritage Science, Springer Nature Journal

• Pierre Monnin: Semantic Web Journal, Journal of Web Semantics, International Journal of Ap-
proximate Reasoning, Annals of Mathematics and Artificial Intelligence, AI Communications,
Communications Medicine, SoftwareX

• Aline Menin: Journal on Virtual Reality

Project WIMMICS

53

• Franck Michel: Biodiversity Data Journal, Data and Knowledge Engineering

• Andrea Tettamanzi: Artificial Intelligence.

• Marco Winckler: SoftwareX, International Journal of Human-Computer Interaction, Information

and Software Technology, Personal and Ubiquitous Computing

• Molka Dhouib: Data and Knowledge Engineering

10.1.4 Invited talks

• Elena Cabrio: “Processing Natural Language to Extract, Analyze and Generate Arguments from
Texts”. SEPLN 2023. 39th International Conference of the Spanish Society for Natural Language
Processing. Jaen (Spain), September 2023.

• Catherine Faron: “Description et interrogation de l’écosystème de graphes de connaissances
D2KAB ” at Séminaire résidentiel INRAE Semantic Linked Data, October 10, 2023, Agde, France.

• Fabien Gandon

– “Walking our Way to the Web” at the Conference on the Web: Scientific Creativity, Technolo-
gical Innovation and Society, XXVIII Conference on Contemporary Philosophy and Methodo-
logy of Science, March 9-10, 2023, University of A Coruña (Spain)

– “Weaving a Web of Augmented Intelligence.”, Keynote Computer Science Insight (CSI) Talk

University St. Gallen, June 29, 2023

– Panel “Conférence IA et Règles européennes”, November 14,2023, Palais des Rois Sardes, Nice

• Pierre Monnin

– Neuro-Symbolic Approaches for the Knowledge Graph Lifecycle. MALOTEC seminar, LORIA,

December 18, 2023, Nancy, France.

– Knowledge Graphs and Analogical Reasoning: from the Example of Zero-Shot KG Bootstrap-
ping to Perspectives. Workshop on analogies: from learning to explainability (supported by
GT MHyIA & EXPLICON – GdR RADIA), November 27, 2023, Arras, France.

– “Using Wikidata to bootstrap a new knowledge graph: selecting relevant entities using analo-
gical pruning” at the Wikidata Modelling Days 2023, Virtual Event, November 30, December
1-2, 2023. Recording.

• Aline Menin: “Visualization of spatio-temporal data: challenges for user perception and represent-

ation.” at the IVU Laboratory seminar. November 20, 2023, Bari, Italy.

• Marco Winckler: “Information Visualisation and Analyticall Provenance: the missing gaps.” at the

IVU Laboratory seminar. October 6, 2023, Bari, Italy.

• Molka Dhouib: “Construction d’un graphe de connaissance à partir des annotations manuelles de
textes de zoologie antique” at Symposium MaDICS, action RoCED. May 24, 2023, Troyes, France.

• Serena Villata

– “Formal and Natural Arguments for Effective Explanations” at the 33rd Int. Conference on

Automated Planning and Scheduling (ICAPS-2023), July 2023, Prague.

– “Artificial Argumentation for Humans” at the franco-japanese conference "Les nouveaux

chemins de l’art et de la culture", October 3, 2023, Tokyo, Japan.

– “Argumentation and Dialogue” at the Ecole d’Automne IA2 in AI and Management of Hetero-

geneous Information and Data, Octobre 30 - Novembre 3, 2023 Le Lazaret, Sète

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Inria Annual Report 2023

10.1.5 Leadership within the scientific community

• Catherine Faron: participant Dagstuhl Seminar, 23081 Agents on the Web 2023-02-19 to 2023-02-24

• Fabien Gandon: participant Dagstuhl Seminar, 23081 Agents on the Web 2023-02-19 to 2023-02-24

• Marco Winckler: Steering Commitee Chair for the IFIP TC13 INTERACT Conference series.

10.1.6 Scientific expertise

• Elena Cabrio: Member of the Comité du suivi doctoral, INRIA Sophia Antipolis; Member of the
Bureau of the Académie 1 of IDEX UCA JEDI; Member of the Conseil d’Administration (CA) of the
French Association of Computational Linguistics (ATALA).

• Catherine Faron: Member of the HCERES comittee in charge of evaluating Paris-Cité training offer;
expert for the European commission (MSCA 2023 and CL4 calls); expert for Institut Français de
Bioinformatique (ELIXIR node)

• Fabien Gandon: European Science Foundation (ESF): Evaluation of a FWO research project, Evalu-

ation of a FWO post-doctoral project; Reviewer PEPR Santé Numérique.

• Serena Villata: Member of the ANR committee to evaluate the research projects on AI (CE23),

Member of the Bureau of CEP of Inria SAM.

• Marco Winckler: Member of the Comité du suivi doctoral (Paris Saclay, UFSCar).

10.1.7 Research administration

• Fabien Gandon : Leader of the Wimmics team ; co-president of scientific and pedagogical council
of the Data Science Technical Institure (DSTI) W3C Advisory Committee Representative (AC Rep)
for Inria.

• Serena Villata: Deputy scientific director of 3IA Côte d’Azur.

• Marco Winckler : Leader of the SPARKS team of the CNRS laboratory I3S (UMR 7271).

10.1.8 Promoting Open Science practices

Too often, the methods described in research papers are not reproducible because the code and/or data
are simply not provided. As a result, it is hardly possible to verify the results and build upon these works.
The Open Science movement is meant to fix this by fostering the unhindered spreading of the results,
methods and products of scientific research. It is based on the open access to publications, data and
source codes.

To make the team members aware of these issues, in 2023 we gave a 2-hour presentation on the
principles of Open Science, the goals of experiment reproducibility, with a focus on practical approaches
meant to make codes and data findable (using metadata), accessible (public repositories and long-time
preservation), referenceable (point to a specific version) and citable (give credit, attribution), as well as
good practices to cite others’ codes and data.

Franck Michel. [article+code+data]: A virtuous tryptic towards reproducible research. 2023. Slides.

10.2 Teaching - Supervision - Juries

10.2.1 Teaching

Participants: Michel Buffa, Elena Cabrio, Olivier Corby, Catherine Faron,
Fabien Gandon, Aline Menin, Amaya Nogales Gómez, An-
drea Tettamanzi, Serena Villata, Marco Winckler, Molka Dhouib,
Benjamin Molinet, Célian Ringwald, Pierre Monnin, Anaïs Ollagnier.

Project WIMMICS

• Michel Buffa:

55

– Licence 3, Master 1, Master 2 Méthodes Informatiques Appliquées à la Gestion des Entreprises

(MIAGE) : Web Technologies, Web Components, etc. 192h.

– DS4H Masters 3D games programming on Web, JavaScript Introduction: 40h.

• Olivier Corby:

– Licence 3 IOTA UniCA 25 hours Semantic Web

– Licence 3 IA DS4H UniCA 25 hours Semantic Web

• Catherine Faron :

– Master 2/5A SI PNS: Web of Data, 32 h

– Master 2/5A SI PNS: Semantic Web 32h

– Master 2/5A SI PNS: Ingénierie des connaissances 15h

– Master DSAI UniCA: Web of Data, 30h

– Master 1/4A SI PNS and Master2 IMAFA/5A MAM PNS: Web languages, 28h

– Licence 3/3A SI PNS and Master 1/4A MAM PNS: Relational Databases, 60h

– Master Data ScienceTech Institute (DSTI): Data pipeline, 50h.

• Fabien Gandon :

– Master: Integrating Semantic Web technologies in Data Science developments, 72 h, M2, Data

ScienceTech Institute (DSTI), France.

– Tutorial Inria Academy - Inria Chile, “CORESE and Semantic Web Standards”, 1h, 10/10/2023

– Tutorial Inria Academy, “CORESE and Semantic Web Standards”, 6h, 28/11/2023

– Inria Academy, “Introduction to CORESE”, Tutorial at World AI Cannes Festival (WAICF),

10/02/2023

– “Introduction à l’Intelligence Artificielle - Représentation et traitement de connaissances”,
Catherine Faron, Fabien Gandon, 3.5 hours, EFELIA Côte d’Azur, 3IA Côte d’Azur, 2/11/2023

• Aline Menin :

– Master 2, Data Visualization, 7h éq. TD (CM/TD), UniCA, MBDS DS4H, France.

– Polytech 5ème année, UniCA, 13.5h (CM/TP), Data visualization.

– BUT 2, IUT Nice Côte d’Azur, 160h éq. TD (CM/TD), “Dévéloppement efficace”, “Qualité de

développement”, and “Dévéloppement des Applications avec IHM”.

• Molinet Benjamin:

– Fabron BUT-1 - Intro NLP - 42h TD.

– EMSI Casablanca, Master IA2 - Natural Language Processing, 20h CM/TD

– Master I Computer Science, Text Processing in AI, 2 hours.

• Amaya Nogales Gómez:

– Master 1, Data Sciences & Artificial Intelligence, UniCA, 20h (CM/TD), Security and Ethical

Aspects of Data.

– Licence 2, Licence Informatique, UniCA, 36h (TP), Structures de données et programmation

C.

• Serena Villata:

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Inria Annual Report 2023

– Master II Droit de la Création et du Numérique - Sorbonne University: Approche de l’Elaboration

et du Fonctionnement des Logiciels, 15 hours (CM), 20 students.

– Master 2 MIAGE IA - University Côte d’Azur: I.A. et Langage : Traitement automatique du

langage naturel, 28 hours (CM+TP), 30 students.

– DUT IA et santé. Natural Language Processing, 4 hours.

• Elena Cabrio:

– Master I Computer Science, Text Processing in AI, 28 hours.

– Master 2 MIAGE IA: I.A. et Langage : Traitement automatique du langage naturel, 15 hours.

– Master 1 EUR CREATES, Parcours Linguistique, traitements informatiques du texte et proces-

sus cognitifs. Introduction to Computational Linguistics, 30 hours.

– DUT IA et santé. Natural Language Processing, 4 hours.

– Licence 3 Sciences et Technologies: parcours Intelligence Artificielle, Natural Language Pro-

cessing, 30 hours.

– Licence 2 IUT, Introduction to AI, 30 hours.

– Licence 1 IUT, Introduction to Database and SQL, 133 hours.

• Andrea Tettamanzi

– Licence: Introduction à l’Intelligence Artificielle, 45 h ETD, L2, UniCA, France.

– Master: Logic for AI, 30 h ETD, M1, UniCA, France.

– Master: Web, 30 h ETD, M1, UniCA, France.

– Master: Algorithmes Évolutionnaires, 24.5 h ETD, M2, UniCA, France.

– Master: Modélisation del l’Incertitude, 24.5 h ETD, M2, UniCA, France.

• Marco Winckler

– Licence 3: Event-driven programming, 45 h ETD, UniCA, Polytech Nice, France.

– Master 1: Methods and tools for technical and scientific writing, Master DSAI, 15 h ETD,

UniCA, DS4H, France.

– Master 2: Introduction to Scientific Research, 15 h ETD, UniCA, Polytech Nice, France.

– Master 2: Information Visualisation, 34 h ETD, UniCA, Polytech Nice, France.

– Master 2: Data Visualization, 15 h ETD, UniCA, MBDS DS4H, France.

– Master 2: Design of Interactive Systems, 34 ETD, UniCA, Polytech Nice, France.

– Master 2: Evaluation of Interactive Systems, 34 ETD, UniCA, Polytech Nice, France.

– Master 2: Multimodal Interaction Techniques, 15 ETD, UniCA, Polytech Nice, France.

– Master 2: coordination of the TER (Travaux de Fin d’Etude), UniCA, Polytech Nice, France.

– Master 2: coordination of the track on Human-Computer Interaction at the Informatics

Department, UniCA, Polytech Nice, France.

• Molka Dhouib

– Licence 3/3A SI PNS: Relational Databases, 32,5h (TD).

– Master 1/4A SI PNS: Web languages, 10h (TD)

– Licence 3/LPI: Introduction to Web of Data and Semantic Web, 16h (TD)

• Célian Ringwald:

– Licence 3/3A SI PNS: XML technologies, 10h (TD).

– Licence 3/3A SI PNS: Bases de données relationnelles, 30h (TD).

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– 1ere Année BUT : Introduction bases de données relationnelles, 40h (TD).

• Pierre Monnin:

– Master 1/Applied Foreign Languages: Introduction to Artificial Intelligence. 12h CM, 12h TD

– Master 1/Adult Education: Introduction to Artificial Intelligence. 12h CM

– Polytech Nice 5th year / EUR DS4H Master 2: Machine Learning & Semantic Web. 1h CM,

1.5h TD

– Polytech Nice 5th year / EUR DS4H Master 2: Machine Learning tutoring. 10h TD

– TELECOM Nancy 3rd year apprenticeship students: Artificial Intelligence. 10h CM, 8h TD

• Anaïs Ollagnier:

– Licence 2 IUT, Introduction to AI, 48h TD.

– Master 1/2/Phd EUR LEXSOCIETE. Introduction to AI Applied to Law and Implications for

Administration and Public Service. 10h CM

– Master 1/2/Phd EUR LEXSOCIETE. Introduction to AI Applied to Law. 12h CM, 8h TD

– Master 1/2/Phd EUR ELMI. Artificial Intelligence and societal tranformation. 10h CM

• Arnaud Barbe:

– 1ere Année BUT : Introduction aux bases de données relationnelles et SQL, 40h TD.

E-learning

• Mooc: Fabien Gandon, Olivier Corby & Catherine Faron, Web of Data and Semantic Web (FR), 7
weeks, FUN, Inria, France Université Numérique, self-paced course 41002, Education for Adults,
17496 learners registered at the time of this report and 855 certificates/badges, MOOC page.

• Mooc: Fabien Gandon, Olivier Corby & Catherine Faron, Introduction to a Web of Linked Data (EN),
4 weeks, FUN, Inria, France Université Numérique, self-paced course 41013, Education for Adults,
5952 learners registered at the time of this report, MOOC page.

• Mooc: Fabien Gandon, Olivier Corby & Catherine Faron, Web of Data (EN), 4 weeks, Coursera,
self-paced course Education for Adults, 5134 learners registered at the time of this report, MOOC
page.

• Mooc: Michel Buffa, HTML5 coding essentials and best practices, 6 weeks, edX MIT/Harvard, self-
paced course Education for Adults, more than 500k learners at the time of this report (2015-2022),
MOOC page.

• Mooc: Michel Buffa, HTML5 Apps and Games, 5 weeks, edX MIT/Harvard, self-paced course
Education for Adults, more than 150k learners at the time of this report (2015-2022), MOOC page.

• Mooc: Michel Buffa, JavaScript Introduction, 5 weeks, edX MIT/Harvard, self-paced course Educa-

tion for Adults, more than 250k learners at the time of this report (2015-2022), MOOC page.

10.2.2 Supervision

PhDs

• PhD in progress: Ali Ballout, Active Learning for Axiom Discovery, Supervised by Andrea Tettamanzi,

UniCA.

• PhD in progress: Lucie Cadorel, Localisation sur le territoire et prise en compte de l’incertitude lors
de l’extraction des caractéristiques de biens immobiliers à partir d’annonces, Supervised by Andrea
Tettamanzi, UniCA.

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Inria Annual Report 2023

• PhD in progress: Rony Dupuy Charles, Combinaison d’approches symboliques et connexionnistes
d’apprentissage automatique pour les nouvelles méthodes de recherche et développement en agro-
végétale-environnement, Supervised by Andrea Tettamanzi, UniCA.

• PhD in progress: Rémi Felin, Découverte évolutive d’axiomes à partir de graphes de connaissances,

UniCA, Co-supervised by Andrea Tettamanzi, Catherine Faron.

• PhD in progress: Pierpaolo Goffredo, Fallacious Argumentation in Political Debates, UniCA 3IA,

Co-supervised by Elena Cabrio and Serena Villata.

• PhD defended: Santiago Marro, Argument-based Explanatory Dialogues for Medicine, UniCA 3IA,

Co-supervised by Elena Cabrio and Serena Villata.

• PhD in progress: Benjamin Molinet, Explanatory argument generation for healthcare applications,

UniCA 3IA, Co-supervised by Elena Cabrio and Serena Villata.

• PhD in progress: Benjamin Ocampo, Subtle and Implicit Hate Speech Detection, UniCA 3IA, Co-

supervised by Elena Cabrio and Serena Villata.

• PhD in progress: Clément Quere. Immersive Visualization Techniques for spatial-temporal data.

Co-supervised by Aline Menin, Hui-Yin Wu, and Marco Winckler.

• PhD in progress: Célian Ringwald, Learning RDF pattern extractors for a language from dual bases
Wikipedia/LOD, Université Côte d’Azur, Co-supervised by Fabien Gandon, Catherine Faron, Franck
Michel, Hanna Abi-Akl.

• PhD in progress: Florent Robert, Analyzing and Understanding Embodied Interactions in Extended

Reality Systems. Co-supervised by Hui-Yin Wu, Lucile Sassatelli, and Marco Winckler.

• PhD in progress: Maroua Tikat, Visualisation multimédia interactive pour l’exploration d’une base
de métadonnées multidimensionnelle de musiques populaires. Co-supervised by Michel Buffa,
Marco Winckler.

• PhD in progress: Xiaoou Wang, Counter-argumentation generation to fight online disinformation,

UniCA, Co-supervised by Elena Cabrio and Serena Villata.

Internships and Apprenticeships

• Apprentice (IUT3) Hugo Carton, “Data visualization”. Co-supervised by Arnaud Barbe, Catherine

Faron, Aline Menin, and Fabien Gandon.

• Apprentice (Polytech, M2) Antoine Vidal-Mazuy, “Conception et implémentation d’un hôte de

plugins audio dans le web”. Co-supervised by Michel Buffa and Marco Winckler.

• Internship (Bachelor MIT) Emma Tysinger “An AI agent to query chemistry knowledge graph”.

Co-supervised by Louis-Félix Nothias and Fabien Gandon.

• Internship (Aix Ynov Campus, M2) Thomas Mac Vicar, “Etude et réalisation d’un outil pour la
conservation, archivage et exposition des projets en réalités étendues”. Co-supervised by Aline
Menin and Marco Winckler.

• Internship (Polytech, M1) Aimane Elfiguigui, “L’usage des glyphes polymorphes pour assister
l’exploration des données spatio-temporelles”. Co-supervised by Aline Menin and Marco Winckler.

• Internship (Polytech, M1) Nicolas Audoux, “Exploration visuelle de données météorologiques”.

Co-supervised by Catherine Faron and Nadia Yacoubi.

• Internship (Polytech, M2) Raphaël Julien. “Creating semantic representations of the real world in

augmented reality: HandyNotes”. Co-supervised by Marco Winckler and Hui-Yin Wu.

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• Internship (CREATES, M1) Irina Gokhaeva, “Cyberharcèlement et discours de haine implicite :
construction, annotation et analyse d’un corpus extrait des réseaux sociaux”. Co-supervised by
Anaïs Ollagnier and Elena Cabrio.

Master Projects (TER/PER Projet d’Etude et de Recherches Polytech)

• Master 2 PER, Polytech: Lynda Attouch. Co-supervised by Catherine Faron, Molka Dhouib and

Pierre Monnin

• Master 2 PER, Polytech: Guillaume Méroué. Supervised by Pierre Monnin

• Master 2 PER, Polytech: Theo Jeannes, Quentin Bourdeau, Ambre Correia. Co-supervised by Aline

Menin and Marco Winckler

• Master 2 PER, Polytech: Christophe Ruiz and Enzo Daval. Co-supervised by Aline Menin and Marco

Winckler

• Master 2 PER, Polytech: Pauline Devictor, Kilian Bonnet, Joel Dibasso, Habib Belmokhtar. Co-

supervised by Marco Winckler, Clément Quéré, and Hui-Yin Wu.

• Master 2 PER, Polytech: Hadyl Ayari. Supervised by Marco Winckler, Maroua Tikat, Catherine

Faron.

• Master 1 Research Project, DS4H: Ekaterina Kostrykina. Co-supervised by Molka Dhouib, Catherine

Faron

• Master 1 Research Project, DS4H: Oumayma Missaoui. Co-supervised by Molka Dhouib, Catherine

Faron

• Master 1 Research Project, DS4H: Karl Alwyn. Co-supervised by Catherine Faron and Franck Michel

• Master 1 Research Project, DS4H: Bachir Benna. Co-supervised by Catherine Faron and Franck

Michel

• Master 1 Research Project, DS4H: Steven Essam. Co-supervised by Molka Dhouib, Catherine Faron

• Master 1 Research Project, DS4H: Hamza Hamdaoui. Co-supervised by Aline Menin and Marco

Winckler

• Master 1 Research Project, DS4H: Hadyl Ayari. Co-supervised by Aline Menin and Marco Winckler.

• Master 1 Research Project, DS4H: Imane El Mountasser. Supervised by Anaïs Ollagnier

• Master 1 Research Project, DS4H: Ahmed Muhammad. Supervised by Anaïs Ollagnier

10.2.3 Juries

• Catherine Faron

– PhD jury member Slimane Makhlouf, “Apprendre à enchérir : prédiction d’évènements rares

et choix de stratégie”, Université Paris Cité, April 19, 2023.

– PhD Comité de Suivi Individuel (CSI) member for Guilherme Santos (U. Toulouse), Nour

Matta (UTT), and Maroua Tikat (UniCA)

• Fabien Gandon

– Reviewer HDR Aurélien Bénel, “Technologies logicielles pour l’instrumentation du travail
intellectuel - Une informatique au service du sens”, Université de Technologie de Troyes,
21/06/2023

– Reviewer HDR Sabrina Kirrane, “Web Knowledge Governance: Legal Knowledge Representa-

tion and Automated Compliance Checking”, WU Vienna University, 20/11/2023

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Inria Annual Report 2023

– PhD Comité de Suivi Individuel (CSI) member for Pierpaolo Goffredo and Kevin Mottin

• Marco Winckler

– PhD Jury member, “An Architecture for Music Recommendation Systems Based on Context of
Interaction and User Experience”. December 20, 2023. Universidade Federal de São Carlos,
Brazil.

– PhD Comité de Suivi Individuel (CSI) member for Maylon PIRES MACEDO, Eliezer Emanuel

BERNART, and Théo BOUGANIM.

• Anaïs Ollagnier, jury de l’édition 2023 du prix de la thèse ATALA décerné conjointement à Tobias
Mayer (I3S, Université Côte d’Azur, Sophia Antipolis) et Thimothée Mickus (ATILF, Université de
Lorraine/CNRS, Nancy).

10.3 Popularization

10.3.1 Articles and contents

• Fabien Gandon: Interview pour “Comment l’IA bouleverse l’art”, Beaux Arts Magazine, May 2023

P.1, 52-56,

• Article in the ERCIM News 133: “IndeGx: An Index of Linked Open Datasets on the Web” by Pierre

Maillot, Catherine Faron, Fabien Gandon and Franck Michel (Inria)

10.3.2 Education

• Anaïs Ollagnier

– “Désinformation et propagation des rumeurs sur les réseaux sociaux”. Lycée Dumont d’Urville,

December 6, 2023, Toulon, France.

– “Validation et diffusion des données scientifiques”. Collège l’Eganaude, April 14, 2023, Biot,

France.

10.3.3 Interventions

• Fabien Gandon: performed 3 sessions “Chiche!” the 24th of January 2023 and 4 other sessions the

8th of December 2023

• Anaïs Ollagnier: “IA et évaluation, outil de triche ou/et de correction ?”. Dans le cadre de la Journée

Pédagogique de l’EUR Arts et Humanités 2023

11 Scientific production

11.1 Major publications

[1] D. Allemang, J. Hendler and F. Gandon. Semantic Web for the Working Ontologist. 3. ACM, June

2020. DOI: 10.1145/3382097. URL: https://hal.inria.fr/hal-02939606.

[2] A. Ben othmane. ‘CARS - A multi-agent framework to support the decision making in uncertain
spatio-temporal real-world applications’. Université Côte d’Azur, 12th Oct. 2017. URL: https://h
al.archives-ouvertes.fr/tel-01680984.

[3] F. Berthelon. ‘Emotion modelization and detection from expressive and contextual data’. Uni-
versité Nice Sophia Antipolis, 16th Dec. 2013. URL: https://tel.archives-ouvertes.fr/tel
-00917416.

[4] K. R. Bouzidi. ‘Semantic web models to support the creation of technical regulatory documents in
building industry’. Université Nice Sophia Antipolis, 11th Sept. 2013. URL: https://tel.archiv
es-ouvertes.fr/tel-00876366.

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[5] E. Cabrio. ‘Artificial Intelligence to Extract, Analyze and Generate Knowledge and Arguments from
Texts to Support Informed Interaction and Decision Making’. Habilitation à diriger des recherches.
Université Côte d’Azur, Oct. 2020. URL: https://hal.inria.fr/tel-03084380.

[6] L. Costabello. ‘Context-aware access control and presentation of linked data’. Université Nice
Sophia Antipolis, 29th Nov. 2013. URL: https://tel.archives-ouvertes.fr/tel-00934617.

[7] P. F. Diallo. ‘Sociocultural and temporal aspects in ontologies dedicated to virtual communities’.
COMUE Université Côte d’Azur (2015 - 2019); Université de Saint-Louis (Sénégal), 16th Sept. 2016.
URL: https://tel.archives-ouvertes.fr/tel-01402394.

[8] A. E. A. Djebri. ‘Uncertainty Management for Linked Data Reliability on the Semantic Web’.
Université Côte D’Azur, 24th Feb. 2022. URL: https://hal.archives-ouvertes.fr/tel-036
79118.

[9] A. Ettorre. ‘Towards an interpretable model of learners in a learning environment based on
knowledge graphs’. Université Côte d’Azur, 28th Nov. 2022. URL: https://theses.hal.scienc
e/tel-04060324.

[10] M. Fell. ‘Natural language processing for music information retrieval : deep analysis of lyrics
structure and content’. Theses. Université Côte d’Azur, May 2020. URL: https://tel.archives-
ouvertes.fr/tel-02587910.

[11] F. Gandon. ‘Distributed Artificial Intelligence And Knowledge Management: Ontologies And Multi-
Agent Systems For A Corporate Semantic Web’. Université Nice Sophia Antipolis, 7th Nov. 2002.
URL: https://tel.archives-ouvertes.fr/tel-00378201.

[12] R. Gazzotti. ‘Knowledge graphs based extension of patients’ files to predict hospitalization’. Theses.
Université Côte d’Azur, Apr. 2020. URL: https://hal.archives-ouvertes.fr/tel-02959420.

[13] P. Goffredo, E. Cabrio, S. Villata, S. Haddadan and J. Torres Sanchez. ‘DISPUTool 2.0: A Modular
Architecture for Multi-Layer Argumentative Analysis of Political Debates’. In: Proceedings of the
AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence. Vol. 37. 13.
Washinghton, DC, United States, 26th June 2023, pp. 16431–16433. DOI: 10.1609/aaai.v37i13
.27069. URL: https://hal.science/hal-04349796.

[14] P. Goffredo, M. Espinoza, S. Villata and E. Cabrio. ‘Argument-based Detection and Classification
of Fallacies in Political Debates’. In: Proceedings of the 2023 Conference on Empirical Methods in
Natural Language Processing. SINGAPORE, Singapore: Association for Computational Linguistics,
Dec. 2023, pp. 11101–11112. DOI: 10.18653/v1/2023.emnlp-main.684. URL: https://hal.s
cience/hal-04260221.

[15] P. Goffredo, S. Haddadan, V. Vorakitphan, E. Cabrio and S. Villata. ‘Fallacious Argument Classifica-
tion in Political Debates’. In: Thirty-First International Joint Conference on Artificial Intelligence
IJCAI-22. Vienna, Austria: International Joint Conferences on Artificial Intelligence Organization,
23rd July 2022, pp. 4143–4149. DOI: 10.24963/ijcai.2022/575. URL: https://hal.science
/hal-03873412.

[16] N. Halliwell. ‘Evaluating and improving explanation quality of graph neural network link predic-
tion on knowledge graphs’. Université Côte d’Azur, 23rd Nov. 2022. URL: https://theses.hal
.science/tel-03907696.

[17] R. Hasan. ‘Predicting query performance and explaining results to assist Linked Data consump-
tion’. Université Nice Sophia Antipolis, 4th Nov. 2014. URL: https://tel.archives-ouvertes
.fr/tel-01127124.

[18] M. Lefrançois. ‘Meaning-Text Theory lexical semantic knowledge representation : conceptualiza-
tion, representation, and operationalization of lexicographic definitions’. Université Nice Sophia
Antipolis, 24th June 2014. URL: https://tel.archives-ouvertes.fr/tel-01071945.

[19] A. Macina. ‘SPARQL distributed query processing over linked data’. COMUE Université Côte d’Azur

(2015 - 2019), 17th Dec. 2018. URL: https://tel.archives-ouvertes.fr/tel-02340700.

[20] N. Marie. ‘Linked data based exploratory search’. Université Nice Sophia Antipolis, 12th Dec. 2014.

URL: https://tel.archives-ouvertes.fr/tel-01130622.

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[21] T. Mayer. ‘Argument Mining on Clinical Trials’. Université Côte d’Azur, 16th Dec. 2020. URL:

https://tel.archives-ouvertes.fr/tel-03209489.

[22] Z. Meng. ‘Temporal and semantic analysis of richly typed social networks from user-generated
content sites on the web’. Université Côte d’Azur, 7th Nov. 2016. URL: https://hal.inria.fr
/tel-01402612.

[23] F. Michel. ‘Integrating heterogeneous data sources in the Web of data’. Université Côte d’Azur,

3rd Mar. 2017. URL: https://tel.archives-ouvertes.fr/tel-01508602.

[24] F. Michel, F. Gandon, V. Ah-Kane, A. Bobasheva, E. Cabrio, O. Corby, R. Gazzotti, A. Giboin, S.
Marro, T. Mayer, M. Simon, S. Villata and M. Winckler. ‘Covid-on-the-Web: Knowledge Graph
and Services to Advance COVID-19 Research’. In: ISWC 2020 - 19th International Semantic Web
Conference. Athens / Virtual, Greece, 1st Nov. 2020. DOI: 10.1007/978-3-030-62466-8_19.
URL: https://hal.archives-ouvertes.fr/hal-02939363.

[25] T. H. Nguyen. ‘Mining the semantic Web for OWL axioms’. Université Côte d’Azur, 2nd July 2021.

URL: https://tel.archives-ouvertes.fr/tel-03406784.

[26] C. Pasquier, C. Da Costa Pereira and A. G. B. Tettamanzi. ‘Extending a Fuzzy Polarity Propagation
Method for Multi-Domain Sentiment Analysis with Word Embedding and POS Tagging’. In: Fron-
tiers in Artificial Intelligence and Applications. ECAI 2020 - 24th European Conference on Artificial
Intelligence. Vol. 325. Santiago de Compostela, Spain: IOS Press, 29th Aug. 2020, pp. 2140–2147.
DOI: 10.3233/FAIA200338. URL: https://hal.inria.fr/hal-02936130.

[27] T. A. Pham. ‘OntoApp : a declarative approach for software reuse and simulation in early stage of
software development life cycle’. Université Côte d’Azur, 22nd Sept. 2017. URL: https://tel.ar
chives-ouvertes.fr/tel-01680766.

[28] O. Seye. ‘Sharing and reusing rules for the Web of data’. Université Nice Sophia Antipolis; Uni-
versité Gaston Berger de Saint Louis, 15th Dec. 2014. URL: https://hal.inria.fr/tel-01096
306.

[29] M. Tounsi Dhouib. ‘Knowledge engineering in the sourcing domain for the recommendation of
providers’. Université Côte d’Azur, 26th Mar. 2021. URL: https://tel.archives-ouvertes.fr
/tel-03336353.

[30] M. Toure. ‘Local peer-to-peer mobile access to linked data in resource-constrained networks’.
Université Côte d’Azur; Université de Saint-Louis (Sénégal), 4th Oct. 2021. URL: https://tel.ar
chives-ouvertes.fr/tel-03514271.

[31] D. M. Tran. ‘Discovering multi-relational association rules from ontological knowledge bases to
enrich ontologies’. Université Côte d’Azur; Université de Danang (Vietnam), 23rd July 2018. URL:
https://tel.archives-ouvertes.fr/tel-01926812.

11.2 Publications of the year

International journals

[32] F. Amardeilh, S. Aubin, S. Bernard, C. Faron, S. Bravo, R. Bossy, F. Michel, J. Raphel and C. Roussey.
‘Combining different points of view on plant descriptions: mapping agricultural plant roles and
biological taxa’. In: Frontiers in Artificial Intelligence. Frontiers in Artificial Intelligence 6 (2023),
p. 118803. DOI: 10.3389/frai.2023.1188036. URL: https://hal.science/hal-04289970.

[33] M. Buffa and J. Lebrun. ‘Rocking the Web with Browser-based Simulations of Tube Guitar Amp-
lifiers’. In: Journal of the Audio Engineering Society 71.11 (16th Nov. 2023), pp. 753–768. URL:
https://inria.hal.science/hal-04335875.

[34]

J.-P. Calbimonte, A. Ciortea, T. Kampik, S. Mayer, T. R. Payne, V. Tamma and A. Zimmermann.
‘Autonomy in the Age of Knowledge Graphs: Vision and Challenges’. In: Transactions on Graph
Data and Knowledge 1.1 (2023), 22p. DOI: 10.4230/TGDK.1.1.13. URL: https://hal.science
/hal-04358408.

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[35]

J. Chen, H. Dong, J. Hastings, E. Jiménez-Ruiz, V. López, P. Monnin, C. Pesquita, P. Škoda and
V. Tamma. ‘Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Op-
portunities’. In: Transactions on Graph Data and Knowledge 1.1 (19th Dec. 2023), pp. 1–33. DOI:
10.4230/TGDK.1.1.5. URL: https://inria.hal.science/hal-04353523.

[36] C. d’Amato, L. Mahon, P. Monnin and G. Stamou. ‘Machine Learning and Knowledge Graphs:
Existing Gaps and Future Research Challenges’. In: Transactions on Graph Data and Knowledge
1.1 (19th Dec. 2023), pp. 1–35. DOI: 10.4230/TGDK.1.1.8. URL: https://inria.hal.science
/hal-04353543.

[37] N. Hubert, P. Monnin, A. Brun and D. Monticolo. ‘Sem@K: Is my knowledge graph embedding
model semantic-aware?’ In: Semantic Web – Interoperability, Usability, Applicability 14.6 (11th Dec.
2023), pp. 1273–1309. DOI: 10.3233/SW-233508. URL: https://inria.hal.science/hal-04
344975.

[38] P. Maillot, O. Corby, C. Faron, F. Gandon and F. Michel. ‘IndeGx: A Model and a Framework for
Indexing RDF Knowledge Graphs with SPARQL-based Test Suits’. In: Journal of Web Semantics
(19th Jan. 2023). DOI: 10.1016/j.websem.2023.100775. URL: https://hal.science/hal-03
946680.

[39] A. Ollagnier, E. Cabrio and S. Villata. ‘Unsupervised Fine-grained Hate Speech Target Community
Detection and Characterisation on Social Media’. In: Social Network Analysis and Mining (3rd Mar.
2023). DOI: 10.1007/s13278-023-01061-4. URL: https://hal.science/hal-04014977.

[40]

S. Pamerlon, A.-S. Archambeau, F. Pando de la Hoz, G. Olivier, F. Michel, S. Tercerie, E. Rodinson, N.
Maurel, G. Martínez-Sagarra, A. Kerner, R. Vignes Lebbe, B. Schatz and P. Dupont. ‘Knowledge Base
on Species Life Traits : A Spanish/French Plinian Core implementation use case’. In: Biodiversity
Information Science and Standards 7 (29th Aug. 2023), e111784. DOI: 10.3897/biss.7.111784.
URL: https://hal.science/hal-04198414.

[41] K. K. Patel, G. Desaulniers, A. Lodi and F. Lecue. ‘Explainable prediction of Qcodes for NOTAMs
using column generation’. In: Journal of the Operational Research Society (2023), pp. 1–11. DOI:
10.1080/01605682.2023.2181715. URL: https://hal.science/hal-04255231.

[42] S. Shen, M. Treleani, D. Compagno and M. Winckler. ‘From Stock Shots to Ghost Data: Tracking
Audiovisual Archives about the European Union’. In: VIEW. Journal of European Television History
and Culture 12.23 (5th Sept. 2023), pp. 4–23. DOI: 10.18146/view.292. URL: https://inria.h
al.science/hal-04233316.

[43] M. Treleani, S. Shen, M. Winckler and A. Menin. ‘Circulation et répétition des images de stock
dans les médias audiovisuels La représentation visuelle des migrants dans les JT européens’. In:
Les Cahiers du numérique 1.19 (2023). DOI: 10.3166/LCN.2023.002. URL: https://hal.scien
ce/hal-04293239.

National journals

[44] A. Toulet, F. Michel, A. Bobasheva, A. Menin, S. Dupré, M.-C. Deboin, M. Winckler and A. Tchechmedjiev.
‘ISSA : un graphe de connaissances au service de la recherche bibliographique’. In: Revue des
Nouvelles Technologies de l’Information 39 (2023), pp. 515–522. URL: https://imt-mines-ales
.hal.science/hal-03969275.

Invited conferences

[45] O. Boissier, P.-A. Champin, A. K. Chopra, N. Fornara, F. Gandon, R. Taelman and J. van den Bussche.
‘Decentralized Hypermedia Ecosystems’. In: Dagstuhl Seminar. Agents on the Web. Vol. 23081.
Wadern, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, pp. 123–126. URL:
https://hal-emse.ccsd.cnrs.fr/emse-04240553.

[46] O. Boissier, A. Ciortea, F. Gandon, S. Mayer and A. Ricci. ‘Hypermedia Multi-Agent Systems’. In:
Dagstuhl Seminar. Agents on the Web. Vol. 23081. Wadern, Germany: Schloss Dagstuhl - Leibniz-
Zentrum für Informatik, 2023, pp. 117–118. URL: https://hal-emse.ccsd.cnrs.fr/emse-04
240524.

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[47] M. Buffa and A. Vidal-Mazuy. ‘High-performance Web-Audio applications, the challenges of
writing a DAW’. In: Réunion de lancement du projet ANR DOTS. paris, France, 13th Oct. 2023.
URL: https://inria.hal.science/hal-04337523.

[48] A. Ciortea, C. Faron, J. Fred Hübner, E. O’Neill, M. Poveda-Villalón, A. Ricci and A. Zimmermann.
‘Abstractions for Agents on the Web’. In: Dagstuhl Seminar. Agents on the Web. Vol. 23081. Wadern,
Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, pp. 126–130. URL: https:
//hal-emse.ccsd.cnrs.fr/emse-04240584.

[49] F. Gandon. ‘Finding the Critical MAS(S): Resources and Representations Needed for Weaving
a Web that Hosts Linked Multi-Agent Systems’. In: Dagstuhl Seminar 23081. Dagstuhl Seminar
23081 : Agents on the Web. Dagstuhl, Germany, 20th Feb. 2023. URL: https://inria.hal.scie
nce/hal-03989703.

International peer-reviewed conferences

[50] R. Abu Ahmad, M. Critelli, ¸S. Efeo˘glu, E. Mancini, C. Ringwald, X. Zhang and A. Meroño Peñuela.
‘Draw Me Like My Triples: Leveraging Generative AI for Wikidata Image Completion’. In: CEUR
Workshop Proceedings. Wikidata 2023 - The 4th Wikidata Workshop (at ISWC 2023). Athenes,
Greece, 7th Nov. 2023. URL: https://hal.science/hal-04262826.

[51] H. A. Akl. ‘A ML-LLM pairing for better code comment classification’. In: FIRE 2023 Working
Notes. FIRE (Forum for Information Retrieval Evaluation) 2023. Goa, India, 31st Dec. 2023. URL:
https://inria.hal.science/hal-04311401.

[52] H. A. Akl. ‘PSYCHIC: A Neuro-Symbolic Framework for Knowledge Graph Question-Answering
Grounding’. In: The First Scholarly QALD Challenge @ ISWC 2023. ISWC 2023 - International
semantic web conference. Athens (Greece), Greece, 1st Dec. 2023. URL: https://inria.hal.sc
ience/hal-04311370.

[53] A. Ballout, C. da Costa Pereira and A. G. B. Tettamanzi. ‘Predicting the Acceptability of Atomic
Candidate OWL Class Axioms’. In: IEEE Xplore. The 22nd IEEE/WIC International Conference on
Web Intelligence and Intelligent Agent Technology. 2023 IEEE International Conference on Web
Intelligence and Intelligent Agent Technology (WI-IAT). Venice, Italy, 2023. DOI: 10.1109/WI-
IAT59888.2023.00055. URL: https://inria.hal.science/hal-04346839.

[54] A. Barisic, P. Sigrist, S. Oliver, A. Sciarra and M. Winckler. ‘Driver Model for Take-Over-Request
in Autonomous Vehicles’. In: UMAP 2023 - 31st ACM Conference on User Modeling, Adaptation
and Personalization. UMAP ’23 Adjunct: Adjunct Proceedings of the 31st ACM Conference on
User Modeling, Adaptation and Personalization. Limassol, Cyprus: ACM, 2023, pp. 317–324. DOI:
10.1145/3563359.3596994. URL: https://hal.science/hal-04205128.

[55] M. Buffa and A. Vidal-Mazuy. ‘WAM-studio, a Digital Audio Workstation (DAW) for the Web’.
In: ACM digital library. WWW 2023 - The ACM Web Conference 2023. WWW ’23 Companion:
Companion Proceedings of the ACM Web Conference 2023. Austin TX USA, United States, 30th Apr.
2023. DOI: 10.1145/3543873.3587987. URL: https://inria.hal.science/hal-04335612.

[56] M. Buffa, A. Vidal-Mazuy, L. May and M. Winckler. ‘WAM-Studio: A Web-Based Digital Audio
Workstation to Empower Cochlear Implant Users’. In: Springer Lecture Notes in Computer Sciences.
INTERACT 2023 - 19th IFIP TC13 International Conference. Vol. LNCS. 14142. Human-Computer
Interaction – INTERACT 2023: 19th IFIP TC13 International Conference, Part I. York, United
Kingdom: Springer Nature Switzerland, 25th Aug. 2023, pp. 101–110. DOI: 10.1007/978-3-031-
42280-5_6. URL: https://inria.hal.science/hal-04233343.

[57] M. Buffa, A. Vidal-Mazuy and Q. Plet. ‘Web Audio Modules: Swiss knife for audio plugin devel-
opments on the Web Platform’. In: h t t p s : // i e e e x p l o r e . i e e e . o r g / x p l / c o n h
o m e / 1 0 3 3 5 1 6 8 / p r o c e e d i n g . Internet of Sounds 2023. Pise, Italy, 26th Oct. 2023. DOI:
10.5281/zenodo.6769408. URL: https://inria.hal.science/hal-04335580.

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[58] L. Cadorel and A. G. B. Tettamanzi. ‘A Comparative Study of Text Representations for French
Real-Estate Classified Advertisements Information Extraction’. In: CEUR Workshop Proceedings.
ENIGMA 2023 : 1st Workshop on AI-driven heterogeneous data management : Completing, mer-
ging, handling inconsistencies and query-answering. Vol. 3495. ENIGMA 2023 - AI-driven hetero-
geneous data management: Completing, merging, handling inconsistencies and query-answering
2023. Rhodes, Greece, 3rd Sept. 2023, pp. 55–63. URL: https://inria.hal.science/hal-0434
6759.

[59] D. El Zein, A. Câmara, C. da Costa Pereira and A. G. B. Tettamanzi. ‘RULKNE: Representing
User Knowledge State in Search-as-Learning with Named Entities’. In: CHIIR 2023 - ACM SIGIR
Conference on Human Information Interaction and Retrieval. CHIIR ’23: Proceedings of the 2023
Conference on Human Information Interaction and Retrieval. Austin, TX, United States: ACM,
19th Mar. 2023, pp. 388–393. DOI: 10.1145/3576840.3578330. URL: https://inria.hal.sci
ence/hal-04152998.

[60] R. Felin, C. Faron and A. G. B. Tettamanzi. ‘A Framework to Include and Exploit Probabilistic
Information in SHACL Validation Reports’. In: IC2023 : 34es Journées francophones d’Ingénierie
des Connaissances. IC 2023 - 34es Journées francophones d’Ingénierie des Connaissances @ Plate-
Forme Intelligence Artificielle (PFIA 2023). IC2023 : 34es Journées francophones d’Ingénierie des
Connaissances. Strasbourg, France, 3rd July 2023. URL: https://inria.hal.science/hal-04
152604.

[61] R. Felin, C. Faron and A. G. B. Tettamanzi. ‘A Framework to Include and Exploit Probabilistic
Information in SHACL Validation Reports’. In: Lecture Notes in Computer Science. ESWC 2023 -
20th International European Semantic Web Conference. Vol. LNCS-13870. The Semantic Web
20th International Conference, ESWC 2023, Hersonissos, Crete, Greece, May 28–June 1, 2023,
Proceedings. Hersonissos, Greece: Springer Nature Switzerland, 2023, pp. 91–104. DOI: 10.1007
/978-3-031-33455-9_6. URL: https://inria.hal.science/hal-04031744.

[62] P. Goffredo, E. Cabrio, S. Villata, S. Haddadan and J. Torres Sanchez. ‘DISPUTool 2.0: A Modular
Architecture for Multi-Layer Argumentative Analysis of Political Debates’. In: Proceedings of the
AAAI Conference on Artificial Intelligence. AAAI-23 - 37th AAAI Conference on Artificial Intelligence.
Vol. 37. AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations 13.
Washinghton, DC, United States, 26th June 2023, pp. 16431–16433. DOI: 10.1609/aaai.v37i13
.27069. URL: https://hal.science/hal-04349796.

[63] P. Goffredo, M. Chaves, S. Villata and E. Cabrio. ‘Argument-based Detection and Classification of
Fallacies in Political Debates’. In: ACL Anthology. EMNLP 2023 - Conference on Empirical Methods
in Natural Language Processing. Vol. 2023.findings-emnlp.684. Findings of the Association for
Computational Linguistics: EMNLP 2023. Singapore (SG), Singapore: Association for Computa-
tional Linguistics, Dec. 2023, pp. 11101–11112. DOI: 10.18653/v1/2023.emnlp-main.684. URL:
https://hal.science/hal-04260221.

[64] F.-Z. Hannou, V. Charpenay, M. Lefrançois, C. Roussey, A. Zimmermann and F. Gandon. ‘The
ACIMOV Methodology: Agile and Continuous Integration for Modular Ontologies and Vocab-
ularies’. In: MK 2023 - 2nd Workshop on Modular Knowledge associated with FOIS 2023 - the
13th International Conference on Formal Ontology in Information Systems. Sherbrooke, Canada,
19th July 2023. URL: https://laas.hal.science/hal-04187236.

[65] N. Hubert, H. Paulheim, P. Monnin, A. Brun and D. Monticolo. ‘Schema First! Learn Versatile
Knowledge Graph Embeddings by Capturing Semantics with MASCHInE’. In: ACM digital library.
K-CAP 2023 - Knowledge Capture Conference 2023. Vol. 2023. K-CAP ’23: Proceedings of the 12th
Knowledge Capture Conference 2023. Pensacola, United States: ACM, 5th Dec. 2023, pp. 188–196.
DOI: 10.1145/3587259.3627550. URL: https://inria.hal.science/hal-04344873.

[66] A. Iglesias-Molina, D. van Assche, J. Arenas-Guerrero, B. de Meester, C. Debruyne, S. Jozashoori,
P. Maria, F. Michel, D. Chaves-Fraga and A. Dimou. ‘The RML Ontology: A Community-Driven
Modular Redesign After a Decade of Experience in Mapping Heterogeneous Data to RDF’. In:
Proceedings of ISWC 2023. ISWC 2023 - 22th International Semantic Web Confernece. Athens,
Greece, 2023. URL: https://hal.science/hal-04201661.

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[67] P. Maillot, O. Corby, C. Faron, F. Gandon and F. Michel. ‘Metadatamatic: A Web application to
Create a Dataset Description’. In: WWW ’23 Companion: Companion Proceedings of the ACM Web
Conference 2023. WWW ’23 - The ACM Web Conference 2023. WWW ’23 Companion: Companion
Proceedings of the ACM Web Conference 2023. Austin TX, United States: ACM, 2023, pp. 123–126.
DOI: 10.1145/3543873.3587328. URL: https://hal.science/hal-04171196.

[68]

S. Marro, T. A. Collias, E. Cabrio and S. Villata. ‘On the Automatic Assessment of Natural Language
Expert Explanations in Medicine’. In: CEUR workshop proceedings. AIxIA 2023 - 22nd International
Conference of the Italian Association for Artificial Intelligence. Vol. 3578. 2nd AIxIA Workshop on
Artificial Intelligence For Healthcare (HC@AIxIA 2023). Rome, Italy, 6th Nov. 2023, pp. 83–98. URL:
https://inria.hal.science/hal-04371254.

[69] S. Marro, B. Molinet, E. Cabrio and S. Villata. ‘Natural Language Explanatory Arguments for
Correct and Incorrect Diagnoses of Clinical Cases’. In: SciTePress digital library. ICAART 2023 -
15th International Conference on Agents and Artificial Intelligence. Vol. 1. Proceedings of the 15th
International Conference on Agents and Artificial Intelligence - (Volume 1). Lisbon (Portugal),
Portugal, 22nd Feb. 2023, pp. 438–449. DOI: 10.5220/0011927000003393. URL: https://hal.s
cience/hal-04002207.

[70] A. Menin, H. Ayari, F. Michel and M. Winckler. ‘Using polymorphic glyphs to support the visual
exploration of hierarchical spatio-temporal data’. In: Lecture notes in computer science. INTER-
ACT 2023 - 19th IFIP International Conference of Technical Committee 13 (Human- Computer
Interaction). Vol. LNCS-14145. Human-Computer Interaction – INTERACT 2023 19th IFIP TC13
International Conference, York, UK, August 28 – September 1, 2023, Proceedings, Part IV. York,
United Kingdom, 28th Aug. 2023, pp. 325–329. DOI: 10.1007/978-3-031-42293-5_31. URL:
https://hal.science/hal-04188896.

[71] F. Michel and F. Gandon. ‘Turn your web APIs into just another piece of your Knowledge Graph’.
In: KGC 2023 - The Knowledge Graph Conference. New York (NY), United States, 12th May 2023.
URL: https://hal.science/hal-04351695.

[72] N. B. Ocampo, E. Cabrio and S. Villata. ‘Playing the Part of the Sharp Bully : Generating Adversarial
Examples for Implicit Hate Speech Detection’. In: Findings of the Association for Computational
Linguistics: ACL 2023. ACL 2023 - 61st Annual Meeting of the Association for Computational
Linguistics. Toronto, Canada: Association for Computational Linguistics, 9th July 2023, pp. 2758–
2772. DOI: 10.18653/v1/2023.findings-acl.173. URL: https://hal.science/hal-04214
125.

[73] N. B. Ocampo, E. Cabrio and S. Villata. ‘Unmasking the Hidden Meaning: Bridging Implicit and
Explicit Hate Speech Embedding Representations’. In: ACL Anthology. EMNLP 2023 - Conference
on Empirical Methods in Natural Language Processing. Vol. 2023.findings-emnlp.441. Findings of
the Association for Computational Linguistics: EMNLP 2023. Singapore, France: Association for
Computational Linguistics, Dec. 2023, pp. 6626–6637. DOI: 10.18653/v1/2023.findings-emnl
p.441. URL: https://hal.science/hal-04351644.

[74] N. B. Ocampo, E. Sviridova, E. Cabrio and S. Villata. ‘An In-depth Analysis of Implicit and Subtle
Hate Speech Messages’. In: ACL Anthology. EACL 2023 - 17th Conference of the European Chapter
of the Association for Computational Linguistics. Vol. 2023. Proceedings of the 17th Conference
of the European Chapter of the Association for Computational Linguistics. Dubrovnik, France:
Association for Computational Linguistics, May 2023, pp. 1997–2013. DOI: 10.18653/v1/2023.e
acl-main.147. URL: https://hal.science/hal-04214094.

[75] A. Ollagnier, E. Cabrio and S. Villata. ‘Harnessing Bullying Traces to Enhance Bullying Participant
Role Identification in Multi-Party Chats’. In: Florida Online Journals. FLAIRS 2023 - 36th Inter-
national conference of the Florida artificial intelligence research sociéty. Florida / USA, United
States, 14th May 2023. URL: https://hal.science/hal-04037120.

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[76] A. Ollagnier, E. Cabrio, S. Villata and S. Tonelli. ‘BiRDy: Bullying Role Detection in Multi-Party
Chats’. In: Proceedings of the AAAI Conference on Artificial Intelligence. AAAI-23 - 37th AAAI
Conference on Artificial Intelligence. Vol. 37. AAAI-23 Special Programs, IAAI-23, EAAI-23, Student
Papers and Demonstrations 13. Washington DC, United States: Association for the Advancement
of Artificial Intelligence, 6th Sept. 2023, pp. 16464–16466. DOI: 10.1609/aaai.v37i13.27080.
URL: https://hal.science/hal-04315527.

[77] M. A. N. Pour, A. Algergawy, P. Buche, L. J. Castro, J. Chen, A. Coulet, J. Cufi, H. Dong, O. Fallatah,
D. Faria, I. Fundulaki, S. Hertling, Y. He, I. Horrocks, M. Huschka, L. Ibanescu, D. S. Jain, E.
Jiménez-Ruiz, N. Karam, P. Lambrix, H. Li, Y. Li, P. Monnin, E. Nasr, H. Paulheim, C. Pesquita,
T. Saveta, P. Shvaiko, G. Sousa, C. Trojahn, J. Vatašˇcinová, M. Wu, B. Yaman, O. Zamazal and L.
Zhou. ‘Results of the Ontology Alignment Evaluation Initiative 2023’. In: Proceedings of the 18th
International Workshop on Ontology Matching co-located with the 22nd International Semantic
Web Conference (ISWC 2023), Athens, Greece, November 7, 2023. 18th International Workshop on
Ontology Matching (OM 2023). Vol. 3591. CEUR workshop proceedings. Athens, Greece: CEUR-
WS.org, 7th Nov. 2023, pp. 97–139. URL: https://inria.hal.science/hal-04366893.

[78] S. Ren, M. Buffa, L. Pottier, Y. Yu and G. Schalk. ‘Reflex-in: Generate Music on the Web with
Real-time Brain Wave’. In: ACM digital library. WWW 2023 - The ACM Web Conference 2023 : Web
Creativity track. WWW ’23 Companion: Companion Proceedings of the ACM Web Conference
2023. Austin TX USA, France, 30th Apr. 2023, pp. 598–600. DOI: 10.1145/3543873.3587315. URL:
https://inria.hal.science/hal-04093045.

[79] F. A. S. Robert, H.-Y. Wu, L. Sassatelli, S. Ramanoel, A. Gros and M. Winckler. ‘An Integrated
Framework for Understanding Multimodal Embodied Experiences in Interactive Virtual Reality’.
In: 2023 IMX - ACM International Conference on Interactive Media Experiences. Nantes, France,
12th June 2023, https://dl.acm.org/conference/imx. DOI: 10.1145/3573381.3596150.
URL: https://hal.science/hal-04102737.

[80] S. Sarkadi and F. Gandon. ‘Interoperable AI for Self-Organisation’. In: SaSSO 2023 Workshop -
Sustainable and Scalable Self-Organisation Workshop (ASCOS 2023). Toronto, Canada, 25th Sept.
2023. DOI: 10.1109/ACSOS-C58168.2023.00043. URL: https://inria.hal.science/hal-0
4384816.

[81] M. Tounsi Dhouib, Q. Merilleau, C. Guerrero, M. Corneli, C. Faron and A. Zucker. ‘Automatic
Semantic Classification of Ancient Zoological Texts’. In: Journal of cultural Heritage. IAMAHA
2023 - 1st International Conference on Artificial Intelligence and Applied Mathematics for History
and Archaeology. Nice, France, 27th Nov. 2023. URL: https://hal.science/hal-04210669.

[82] E. Tysinger, M. Pagni, O. Kirchhoffer, F. Mehl, F. Gandon, J.-L. Wolfender and L.-F. Nothias. ‘An
Artificial Intelligence Agent for Navigating Knowledge Graph Experimental Metabolomics Data’.
In: Frontiers in Metabolomics - Book of abstract -. 2023 Swiss Metabolomics Society Annual
Meeting. Frontiers in Metabolomics. Zurich, Switzerland, 15th Sept. 2023. URL: https://hal.sc
ience/hal-04381448.

[83] N. Yacoubi Ayadi, C. Faron, F. Michel, F. Gandon and O. Corby. ‘Computing and Visualizing Agro-
Meteorological Parameters based on an Observational Weather Knowledge Graph’. In: Companion
Proceedings of the ACM Web Conference 2023. WWW 2023 - ACM Web Conference 2023 - Interna-
tional World Wide Web Conference. WWW ’23 Companion: Companion Proceedings of the ACM
Web Conference 2023. Austin (TX), United States: ACM, 30th Apr. 2023. DOI: 10.1145/3543873
.3587357. URL: https://hal.science/hal-04230234.

[84] A. Zimmermann, A. Ciortea, C. Faron, E. O’Neill and M. Poveda-Villalón. ‘Pody: a Solid-based ap-
proach to embody agents in Web-based Multi-Agent-Systems’. In: EMAS 2023 - 11th International
Workshop on Engineering Multi-Agent Systems. Londres, United Kingdom, 29th May 2023. URL:
https://hal-emse.ccsd.cnrs.fr/emse-04058783.

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National peer-reviewed Conferences

[85] A. Barbe, M. Tounsi Dhouib, C. Faron, M. Corneli and A. Zucker. ‘Building a knowledge graph
from manual annotations of ancient zoology texts’. In: IC 2023 - 34es Journées francophones
d’Ingénierie des Connaissances @ Plate-Forme Intelligence Artificielle (PFIA 2023). IC2023 : 34es
Journées francophones d’Ingénierie des Connaissances. Starsbourg, France, 3rd July 2023. URL:
https://inria.hal.science/hal-04156996.

[86] L. Cadorel, A. G. B. Tettamanzi and F. Gandon. ‘Graphes de Connaissances et Ontologie pour la
Représentation de Données Immobilières Issues d’Annonces en Texte Libre’. In: IC 2023 - 34es
Journées francophones d’Ingénierie des Connaissances @ Plate-Forme Intelligence Artificielle
(PFIA 2023). IC2023 : 34es Journées francophones d’Ingénierie des Connaissances. Strasbourg,
France, 3rd July 2023. URL: https://inria.hal.science/hal-04153252.

[87] M. Tounsi Dhouib, E. Kostrykina and C. Faron. ‘Automatic definition of the level of textual dif-
ficulty of documents’. In: Revue des Nouvelles Technologies de l’Information. 24ème conférence
francophone sur l’Extraction et la Gestion des Connaissances. Dijon, France, 2024. URL: https:
//hal.science/hal-04353060.

[88] A. Vidal-Mazuy and M. Buffa. ‘A Digital Audio Workstation for the Web Platform’. In: Actes des
journées d’informatique musicale 2023. Journées d’Informatique Musicale 2023. Vol. Pages 191-199.
Paris, France, 26th May 2023, p. 8. URL: https://inria.hal.science/hal-04334794.

Conferences without proceedings

[89] E. Puech, A. Barbe, M. Corneli and I. Théry-Parisot. ‘A combined database of anatomical features
and micrographs for selected Southern African woods and charcoals: a perspective on an iden-
tification key for anthracological study’. In: XXIVe colloque du GMPCA (Groupe des Méthodes
Pluridisciplinaires Contribuant à l’Archéologie) : Archéométrie 2023. Nice, France, 17th Apr. 2023.
URL: https://hal.science/hal-04122866.

Scientific book chapters

[90] A. Barisic and M. Winckler. ‘Towards User Profile Meta-Ontology’. In: Human-Centered Software
Engineering. HCSE 2023. Lecture Notes in Computer Science. LNCS. 31st Oct. 2023. URL: https:
//hal.science/hal-04210148.

[91] M. Buffa and J. Lebrun. ‘A browser-based WebAudio ecosystem to play real-time with simulations
of historic guitar tube amps and their typical distortions’. In: Distortion in Music Production . The
Soul of Sonics. Taylor and Francis publishers, 2nd June 2023, pp. 28–46. DOI: 10.4324/97804293
56841. URL: https://inria.hal.science/hal-04337603.

[92] A. Menin, P. Maillot, C. Faron, O. Corby, C. M. Dal Sasso Freitas, F. Gandon and M. Winckler. ‘LDViz:
a tool to assist the multidimensional exploration of SPARQL endpoints’. In: Web Information
Systems and Technologies : 16th International Conference, WEBIST 2020, November 3–5, 2020, and
17th International Conference, WEBIST 2021, October 26–28, 2021, Virtual Events, Revised Selected
Papers. Vol. LNBIP - 469. LNBIP - Lecture Notes in Business Information Processing. Springer,
2023, pp. 149–173. DOI: 10.1007/978-3-031-24197-0. URL: https://hal.science/hal-039
29913.

Edition (books, proceedings, special issue of a journal)

[93]

J. Abdelnour Nocera, M. Kristín Lárusdóttir, H. Petrie, A. Piccinno and M. Winckler, eds. Human-
Computer Interaction – INTERACT 2023 : part I: 19th IFIP TC13 International Conference, York, UK,
August 28 – September 1, 2023, Proceedings, Part I. Vol. LNCS-14142. Lecture Notes in Computer
Science. Springer Nature Switzerland, 2023. DOI: 10.1007/978-3-031-42280-5. URL: https:
//inria.hal.science/hal-04233377.

Project WIMMICS

69

[94]

[95]

[96]

[97]

J. Abdelnour Nocera, M. Kristín Lárusdóttir, H. Petrie, A. Piccinno and M. Winckler, eds. Human-
Computer Interaction – INTERACT 2023 : part II: 19th IFIP TC13 International Conference, York, UK,
August 28 – September 1, 2023, Proceedings, Part II. Vol. LNCS-14143. Lecture Notes in Computer
Science. Springer Nature Switzerland, 2023. DOI: 10.1007/978-3-031-42283-6. URL: https:
//inria.hal.science/hal-04233382.

J. Abdelnour Nocera, M. Kristín Lárusdóttir, H. Petrie, A. Piccinno and M. Winckler, eds. Human-
Computer Interaction – INTERACT 2023 : part III: 19th IFIP TC13 International Conference, York,
UK, August 28 – September 1, 2023, Proceedings, Part III. Vol. LNCS-14144. Lecture Notes in
Computer Science. Springer Nature Switzerland, 2023. DOI: 10.1007/978-3-031-42286-7. URL:
https://inria.hal.science/hal-04233383.

J. Abdelnour Nocera, M. Kristín Lárusdóttir, H. Petrie, A. Piccinno and M. Winckler, eds. Human-
Computer Interaction – INTERACT 2023 : part IV: 19th IFIP TC13 International Conference, York,
UK, August 28 – September 1, 2023, Proceedings, Part IV. Vol. LNCS-14145. Lecture Notes in
Computer Science. Springer Nature Switzerland; Springer Nature Switzerland, 2023. DOI: 10.100
7/978-3-031-42293-5. URL: https://inria.hal.science/hal-04233389.

S. Benferhat, G. Casini, T. Meyer and A. G. B. Tettamanzi, eds. ENIGMA 2023 : AI-driven heterogen-
eous data management: Completing, merging, handling inconsistencies and query-answering 2023:
Proceedings of 1st Workshop on AI-driven heterogeneous data management: Completing, merging,
handling inconsistencies and query-answering,co-located with 20th International Conference on
Principles of Knowledge Representation and Reasoning (KR 2023)Rhodes, Greece, September 3-4,
2023. ENIGMA 2023 - 1st Workshop on AI-driven heterogeneous data management: Completing,
merging, handling inconsistencies and query-answering. Vol. 3495. ENIGMA 2023 - AI-driven
heterogeneous data management: Completing, merging, handling inconsistencies and query-
answering 2023. CEUR-WS.org, 2023. URL: https://inria.hal.science/hal-04346716.

[98] A. Dix, M. Winckler and M. Jones, eds. EICS ’23 Companion: Companion Proceedings of the
2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems. ACM, 2023. DOI:
10.1145/3596454. URL: https://inria.hal.science/hal-04233375.

[99] C. Faron and S. Loudcher, eds. Extraction et Gestion des Connaissances : EGC 2023. Vol. E39.

Editions RNTI, 2023. URL: https://hal.science/hal-04169619.

Doctoral dissertations and habilitation theses

[100]

S. Marro. ‘Argumentation quality : from general principles to healthcare applications’. Université
Côte d’Azur, 8th Nov. 2023. URL: https://theses.hal.science/tel-04402347.

Reports & preprints

[101] M. Toure, F. Gandon, K. Guidedi, M. Lo and C. Guéret. Survey of Models and Architectures to
Ensure Linked Data Access. Inria & Université Côte d’Azur, CNRS, I3S, 23rd Jan. 2023, p. 23. URL:
https://inria.hal.science/hal-03956456.

Other scientific publications

[102] A. Barbe, M. T. Dhouib, C. Faron, M. Corneli and A. Zucker. ‘Zoo-KG: A knowledge graph of
zoological data’. In: AIUCD 2023 - Associazione per l’Informatica Umanistica e la Cultura Digitale.
Sienna (Italy), Italy, 5th June 2023. URL: https://hal.science/hal-04235805.

[103]

S. Ouelhadj, P.-A. Champin, S. Jean-Daubias and J. Gaillard. ‘A method to semantically enrich data
using annotated UML’. In: EGC 2023 - Extraction et Gestion des Connaissances. Vol. RNTI-E-39.
Lyon, France, 18th Jan. 2023, pp. 635–636. URL: https://hal.science/hal-04021565.

[104] C. Ringwald, F. Gandon, C. Faron, F. Michel and H. A. Akl. ‘Learning RDF pattern extractors from
natural language and knowledge graphs -application to Wikipedia and the LOD’. In: ISWS 2023
- International Semantic Web Research Summer School / GEMSS 2023 - Generative Modeling
Summer School. Bertinoro, Italy / Copenhague, Denmark, June 2023. URL: https://hal.scienc
e/hal-04175511.

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11.3 Other

Scientific popularization

[105] M. Buffa and J.-F. Trubert. ‘Playing with Web Audio Modules: the Concept of Distributive Creation
and Live Performance’. In: Digital Week Conference 2023. Nice, France, 12th June 2023. URL:
https://inria.hal.science/hal-04337545.

[106] C. Ringwald, F. Gandon, C. Faron, F. Michel and H. Abi Akl. ‘Well-Written Knowledge Graphs: Most
Effective RDF Syntaxes for Triple Linearization in End-to-End Extraction of Relations from Texts
(Student Abstract)’. In: AAAI 2024. Vancouver (BC), Canada, 27th Feb. 2024. URL: https://hal.s
cience/hal-04384724.

Softwares

[107]

[SW ] R. Cérès, O. Corby, E. Demairy and F. Gandon, Corese version 4.5.0, 25th July 2023. Inria;
CNRS; Université Côte d’Azur. LIC: CECILL-C. DOI: 10.5281/zenodo.10376241, HAL: 〈hal-041
70333〉, URL: https://hal.science/hal-04170333, VCS: https://github.com/Wimmics/c
orese, SWHID: 〈swh:1:dir:7ec9a0bcccba1254457985de26d524d0d5c4a9c5〉.

11.4 Cited publications

[108] R. Borgo, J. Kehrer, D. H. S. Chung, E. Maguire, R. S. Laramee, H. Hauser, M. Ward and M. Chen.
‘Glyph-based Visualization: Foundations, Design Guidelines, Techniques and Applications’. In:
Eurographics State of the Art Reports (2013). ISBN: 1558608192, pp. 39–63. URL: http://www.cg
.tuwien.ac.at/research/publications/2013/borgo-2013-gly/.

[109] A. Ciortea, S. Mayer, S. Bienz, F. Gandon and O. Corby. ‘Autonomous search in a social and
ubiquitous Web’. In: Personal and Ubiquitous Computing (June 2020). DOI: 10.1007/s00779-02
0-01415-1. URL: https://hal.inria.fr/hal-03135088.

[110] F. Gandon. ‘The three ’W’ of the World Wide Web call for the three ’M’ of a Massively Multidiscip-
linary Methodology’. In: 10th International Conference, WEBIST 2014. Ed. by V. Monfort and
K.-H. Krempels. Vol. 226. Web Information Systems and Technologies. Barcelona, Spain: Springer
International Publishing, Apr. 2014. DOI: 10.1007/978-3-319-27030-2. URL: https://hal.i
nria.fr/hal-01223236.

[111] F. Gandon, M. Buffa, E. Cabrio, O. Corby, C. Faron Zucker, A. Giboin, N. Le Thanh, I. Mirbel, P.
Sander, A. G. B. Tettamanzi and S. Villata. ‘Challenges in Bridging Social Semantics and Formal
Semantics on the Web’. In: 5h International Conference, ICEIS 2013. Ed. by S. Hammoudi, J.
Cordeiro, L. Maciaszek and J. Filipe. Vol. 190. Angers, France: Springer, July 2013, pp. 3–15. URL:
https://hal.inria.fr/hal-01059273.

[112] T. Munzner. Visualization analysis and design. CRC press, 2014.

[113] A. Ollagnier, E. Cabrio, S. Villata and C. Blaya. ‘CyberAgressionAdo-v1: a Dataset of Annotated
Online Aggressions in French Collected through a Role-playing Game’. In: LREC 2022 - 13th
Language Resources and Evaluation Conference. Vol. 2022.lrec-1.91. Marseille, France, June 2022,
pp. 867–875. URL: https://hal.science/hal-03765860.

[114] A. Toulet, F. Michel, A. Bobasheva, A. Menin, S. Dupré, M.-C. Deboin, M. Winckler and A. Tchechmedjiev.
‘ISSA: Generic Pipeline, Knowledge Model and Visualization tools to Help Scientists Search and
Make Se,nse of a Scientific Archive’. In: ISWC 2022 - 21st International Semantic Web Conference.
Hangzhou, China, Oct. 2022. DOI: 10.1007/978-3-031-19433-7\_38. URL: https://hal.sci
ence/hal-03807744.

[115] N. Yacoubi Ayadi, C. Faron, F. Michel, F. Gandon and O. Corby. ‘A Model for Meteorological Know-
ledge Graphs: Application to Météo-France Data’. In: ICWE 2022- 22nd International Conference
on Web Engineering. 22nd International Conference on Web Engineering, ICWE 2022. Bari, Italy,
July 2022. URL: https://inria.hal.science/hal-03619869.

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71

[116] N. Yacoubi Ayadi, C. Faron, F. Michel, F. Gandon and O. Corby. WeKG-MF: a Knowledge Graph of
Observational Weather Data. ESWC 2022 - 19th Extended Semantic Web Conference. Poster. May
2022. DOI: 10.1007/978-3-031-11609-4\_19. URL: https://inria.hal.science/hal-036
57694.
<final_summary>
The WIMMICS project is a research initiative focused on reconciling social and formal semantics on the World Wide Web. The project aims to develop models and methods to bridge the gap between formal semantics and social semantics on the Web, enabling seamless and meaningful interactions between computational and natural intelligence. WIMMICS proposes multidisciplinary approaches to analyze and model the many aspects of the intertwined information systems, their communities of users, and their interactions. The project also focuses on knowledge representation, reasoning, and artificial intelligence formalisms on the Web, with a particular emphasis on semantic Web, linked data, and knowledge graphs. The WIMMICS project has several application domains, including social semantic Web, linked data on the Web and on intranets, assisting Web-based epistemic communities, and analyzing scientific co-authorship. The project has also led to the development of several software platforms, including CORESE, DBpedia, and ISSA-pipeline, which provide tools for semantic indexing, data visualization, and knowledge graph construction. Overall, the WIMMICS project seeks to advance our understanding of the complex interactions between humans and the Web, and to develop innovative solutions for harnessing the potential of the Web to support human decision-making and knowledge creation.</final_summary>
The WIMMICS project is a research initiative focused on reconciling social and formal semantics on the World Wide Web. The project aims to develop models and methods to bridge the gap between formal semantics and social semantics on the Web, enabling seamless and meaningful interactions between computational and natural intelligence. WIMMICS proposes multidisciplinary approaches to analyze and model the many aspects of the intertwined information systems, their communities of users, and their interactions. The project also focuses on knowledge representation, reasoning, and artificial intelligence formalisms on the Web, with a particular emphasis on semantic Web, linked data, and knowledge graphs. The WIMMICS project has several application domains, including social semantic Web, linked data on the Web and on intranets, assisting Web-based epistemic communities, and analyzing scientific co-authorship. The project has also led to the development of several software platforms, including CORESE, DBpedia, and ISSA-pipeline, which provide tools for semantic indexing, data visualization, and knowledge graph construction. Overall, the WIMMICS project seeks to advance our understanding of the complex interactions between humans and the Web, and to develop innovative solutions for harnessing the potential of the Web to support human decision-making and knowledge creation.