RESEARCH CENTRE

Inria Saclay Centre
at Institut Polytechnique de
Paris

IN PARTNERSHIP WITH:

Institut Polytechnique de Paris, CNRS

2023
ACTIVITY REPORT

Project-Team

CEDAR

Rich Data Exploration at Cloud Scale

IN COLLABORATION WITH: Laboratoire d’informatique de l’école
polytechnique (LIX)

DOMAIN

Perception, Cognition and Interaction

THEME

Data and Knowledge Representation and
Processing

Contents

Project-Team CEDAR

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

3.1 Multi-model querying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Exploratory querying of data graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 An unified framework for optimizing data analytics . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Elastic resource management for virtualized database engines . . . . . . . . . . . . . . . . .
3.5 Argumentation mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6 Measuring and mitigating risks of AI-driven information targeting . . . . . . . . . . . . . . .

4 Application domains

4.1 Cloud computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Computational journalism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Computational social science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Online targeted advertising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Social and environmental responsibility

5.1 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 Highlights of the year

6.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Engagement with journalists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 New software, platforms, open data

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7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.1 ConnectionLens
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7.1.2 Abstra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
7.1.3 StatCheck . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
7.1.4 ConnectionStudio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
7.1.5 FactSpotter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.6 PathWays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7.1.7 OpenIEEntity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

8 New results

10
8.1 Data management for analyzing digital arenas . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
8.1.1 Graph integration of heterogeneous data sources for data journalism . . . . . . . . . 10
8.1.2 Fact-checking Multidimensional Statistic Claims in French . . . . . . . . . . . . . . . 11
8.2 Online targeted advertising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.2.1 On Detecting Policy-Related Political Ads: An Exploratory Analysis of Meta Ads

during the 2022 French Election . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.2.2 Collaborative Ad Transparency: Promises and Limitations . . . . . . . . . . . . . . . . 12
8.2.3 Marketing to Children Through Online Targeted Advertising: Targeting Mechanisms

and Legal Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
8.2.4 Understanding the privacy risks of popular search engine advertising systems . . . . 12
8.3 How effective are tracking restrictions: A case study on Meta . . . . . . . . . . . . . . . . . . 13
8.4 Improving the quality of public debate with AI . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.4.1 Argumentation mining and applications to public discourse . . . . . . . . . . . . . . 13
8.4.2 Finding conflicts of interest via open information extraction with entity-focused

constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.4.3 Detecting hallucinations in Large Language Models . . . . . . . . . . . . . . . . . . . 14
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

8.5 Efficient Big Data analytics

8.5.1 Efficient and robust active learning methods for interactive database exploration . . 14
8.5.2 Multi-Objective Optimization for Spark-based Data Analytics . . . . . . . . . . . . . . 14
8.5.3 Scalable Analytics on Multi-Streams Dynamic Graphs . . . . . . . . . . . . . . . . . . 15
8.6 Explainable Anomaly Detection on Multivariate Time Series in the AIOps Domain . . . . . 15

9 Partnerships and cooperations

9.1 European initiatives

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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.1.1 Horizon Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.1.2 H2020 projects
9.2 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.2.1 ANR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
9.2.2 Others
9.3 Regional initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

10 Dissemination

16
10.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.1.3 Journal
10.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.1.5 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 18
10.1.6 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.1.7 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
10.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
10.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
10.3.1 Internal or external Inria responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . 20
10.3.2 Articles and contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10.3.3 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

11 Scientific production

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11.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.2 Publications of the year

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Project-Team CEDAR

Creation of the Project-Team: 2018 April 01

Keywords

Computer sciences and digital sciences

A3.1.1. – Modeling, representation

A3.1.2. – Data management, quering and storage

A3.1.3. – Distributed data

A3.1.6. – Query optimization

A3.1.7. – Open data

A3.1.8. – Big data (production, storage, transfer)

A3.1.9. – Database

A3.2.1. – Knowledge bases

A3.2.3. – Inference

A3.2.4. – Semantic Web

A3.2.5. – Ontologies

A3.3. – Data and knowledge analysis

A3.3.1. – On-line analytical processing

A3.3.2. – Data mining

A3.3.3. – Big data analysis

A3.4.1. – Supervised learning

A3.4.6. – Neural networks

A3.4.8. – Deep learning

A9.1. – Knowledge

A9.2. – Machine learning

Other research topics and application domains

B6.5. – Information systems

B8.5.1. – Participative democracy

B9.5.6. – Data science

B9.7.2. – Open data

B9.10. – Privacy

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Ioana Manolescu [Team leader, INRIA, Senior Researcher, HDR]

• Oana-Denisa Balalau [INRIA, ISFP]

• Oana Goga [CNRS, Researcher]

Faculty Member

• Yanlei Diao [Ecole Polytechnique, Professor, HDR]

Post-Doctoral Fellows

• Madhulika Mohanty [INRIA, Post-Doctoral Fellow, until Feb 2023]

• Sein Minn Sein Minn [CNRS, until Jan 2023]

PhD Students

• Nardjes Amieur [CNRS, from Feb 2023]

• Nelly Barret [INRIA]

• Theo Bouganim [INRIA]

• Tom Calamai [Amundi, CIFRE]

• Salim Chouaki [CNRS]

• Asmaa El Fraihi [CNRS, from Feb 2023]

• Qi Fan [Ecole Polytechnique,LIX]

• Mhd Yamen Haddad [INRIA, until Jan 2023]

• Vincent Jacob [Ecole Polytechnique]

• Muhammad Khan [INRIA]

• Kun Zhang [INRIA]

Technical Staff

• Simon Ebel [INRIA, Engineer]

• Theo Galizzi [INRIA, Engineer]

• Madhulika Mohanty [INRIA, Engineer, from Mar 2023]

• Camille Pettineo [INRIA, Engineer, from Feb 2023 until Apr 2023]

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Interns and Apprentices

• Abir Benzaamia [CNRS, Intern, from Feb 2023 until Jul 2023]

• Louis Caubet [INRIA, Intern, until Mar 2023]

• Ahmed-Yassine Chraa [ECOLE POLY PALAISEAU, Intern, from Jun 2023 until Jul 2023]

• Nikola Dobricic [INRIA, Intern, from Jul 2023 until Aug 2023]

• Philippe Guyard [Ecole Polytechnique, LIX, Intern, until Mar 2023]

• Amine Lamouchi [Ecole Polytechnique, LIX, Intern, until Mar 2023]

• Melissa Mokhtari [CNRS, Intern, from Feb 2023 until Jul 2023]

• Maria-Natalia Osman-Calescu [INRIA, Intern, from Jul 2023 until Aug 2023]

• Ines Vignal [Ecole Polytechnique, LIX, Intern, until Mar 2023]

Administrative Assistant

• Michael Barbosa [INRIA]

Visiting Scientists

• Andrei-Laurentiu Bornea [Ecole Polytechnique, LIX, from Feb 2023 until Aug 2023]

• Vera Sosnovik [ECOLE POLY PALAISEAU, from Apr 2023 until Jun 2023]

External Collaborators

• Matthieu Beauval [Radio France]

• Mathilde Bouquerel [Radio France, until Nov 2023]

• Estelle Cognacq [Radio France, until Aug 2023]

• Antoine Deiana [Radio France]

• Emilie Gautreau [Radio France]

• Samuel Guimaraes [CNRS, from Nov 2023]

• Stéphane Horel [LE MONDE]

• Antoine Krempf [Radio France, until Mar 2023]

• Chenghao Lyu [Ecole Polytechnique, LIX]

• Isotta Magistrali [IP PARIS, from Oct 2023 until Nov 2023]

• Adrien Maumy [Radio France]

• Gabrielle Mura [IP PARIS, from Oct 2023 until Nov 2023]

• Thomas Pontillon [Radio France, until Apr 2023]

• Gerald Roux [Radio France]

• Saumya Yashmohini Sahai [OSU, until Mar 2023]

• Prajna Devi Upadhyay [BITS PILANI HYDERABAD CAMPUS]

• Joanna Yakin [Radio France, until Apr 2023]

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Inria Annual Report 2023

2 Overall objectives

Our research aims at models, algorithms and tools for highly efficient, easy-to-use data and knowledge
management; throughout our research, performance at scale is a core concern, which we address,
among other techniques, by designing algorithms for a cloud (massively parallel) setting. In addition, we
explore and mine rich data via machine learning techniques. Our scientific contributions fall into four
interconnected areas:

Optimization and performance at scale. This topic is at the heart of Y. Diao’s ERC project “Big and Fast
Data”, which aims at optimization with performance guarantees for real-time data processing in
the cloud. Machine learning techniques and multi-objective optimization are leveraged to build
performance models for cloud data analytics. The same goal is shared by our work on the efficient
evaluation of queries in dynamic knowledge bases.

Data discovery and exploration. Today’s Big Data is complex; understanding and exploiting it is daunt-
ing, especially to novice users such as journalists or domain scientists. To help such users, in the AI
Chair "SourcesSay: Intelligent Data Analysis and Interconnection in Digital Arenas", we explore
efficient and keyword search techniques to find answers in the data its highly heterogeneous struc-
ture makes standard (e.g., SQL) queries inapplicable. Further, we propose novel data abstraction
methods, which, given a dataset, automatically compute a simple, human-understandable model
thereof. Finally, we study heterogeneous graph exploration, blending graph querying, and natural
language summarization.

Natural language understanding for analyzing and supporting digital arenas. In this area, we are fo-
cused on new natural langauge processing tools and their applications to problems such as ar-
gumentation mining, factfulness evaluation and information extraction. We are interested in
particular on applications with high social value, such as analysing public discourse with the
goal of finding elements that could bias the world view of citizens, such as false claims, fallacious
arguments, propaganda, or greenwashing.

Safeguarding information systems. Recent events have brought to light the easiness of using current
online systems to propagate information (that is sometimes false) and that we are facing an
information war. We create knowledge and technology in this area to make the online information
space safer. In O. Goga’s ERC project "Momentous: Measuring and Mitigating Risks of AI-driven
Information Targeting", we seek to use AI for good to help fact-checking and journalists, we develop
natural language processing techniques to detect malicious online content (e.g., propaganda,
manipulation), and we develop measurement methodologies and controlled experiments to assess
risks with online systems.

3 Research program

3.1 Multi-model querying

As the world’s affairs get increasingly more digital, a large and varied set of data sources becomes available:
they are either structured databases, such as government-gathered data (demographics, economics,
taxes, elections, ...), legal records, stock quotes for specific companies, un-structured or semi-structured,
including in particular graph data, sometimes endowed with semantics (see e.g., the Linked Open
Data cloud). Modern data management applications, such as data journalism, are eager to combine in
innovative ways both static and dynamic information coming from structured, semi-structured, and
unstructured databases and social feeds. However, current content management tools for this task are
not suited for the task, in particular when they require a lengthy rigid cycle of data integration and
consolidation in a warehouse. Thus, we need flexible tools allowing us to interconnect various kinds of
data sources and query them together.

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3.2 Exploratory querying of data graphs

Semantic graphs, including data and knowledge, are hard to apprehend for users due to the complexity
of their structure and, often to their large volumes. To help tame this complexity, our research follows
several avenues. First, we build compact summaries of Semantic Web (RDF) graphs suited for a first-sight
interaction with the data. Second, we devise fully automated methods of exploring RDF graphs using
interesting aggregate queries, which, when evaluated over a given input graph, yield interesting results
(with interestingness understood in a formal, statistical sense). Third, we study the exploration of highly
heterogeneous data graphs resulting from integrating structured, semi-structured, and unstructured
(text) data. In this context, we develop data abstraction methods, showing the structure of any dataset to a
novice user, as well as searching on the graph through (i ) keyword queries and (i i ) exploration leveraging
graph structure and linguistic contents.

3.3 An unified framework for optimizing data analytics

Data analytics in the cloud has become an integral part of enterprise businesses. Big data analytics
systems, however, still lack the ability to take user performance goals and budgetary constraints for a
task collectively referred to as task objectives, and automatically configure an analytic job to achieve the
objectives. Our goal is to develop a data analytics optimizer that can automatically determine a cluster
configuration with a suitable number of cores and other runtime system parameters that best meet the
task objectives. To achieve this, we also need to design a multi-objective optimizer that constructs a Pareto
optimal set of job configurations for task-specific objectives and recommends new job configurations to
best meet these objectives.

3.4 Elastic resource management for virtualized database engines

Database engines are migrating to the cloud to leverage the opportunities for efficient resource man-
agement by adapting to the variations and heterogeneity of the workloads. Resource management in a
virtualized setting, like the cloud, must be enforced in a performance-efficient manner to avoid introdu-
cing overheads to the execution. We design elastic systems that change their configuration at runtime
with minimal cost to adapt to the workload every time. Changes in the design include both different
resource allocations and different data layouts. We consider different workloads, including transactional,
analytical, and mixed, and we study the performance implications on different configurations to propose
a set of adaptive algorithms.

3.5 Argumentation mining

Argumentation appears when we evaluate the validity of new ideas, convince an addressee, or solve a
difference of opinion. An argument contains a statement to be validated (a proposition also called claim
or conclusion), a set of backing propositions (called premises, which should be accepted ideas), and
a logical connection between all the pieces of information presented that allows the inference of the
conclusion. In our work, we focus on fallacious arguments, where evidence does not prove or disprove
the claim, for example, in an "ad hominem" argument, a claim is declared false because the person
making it has a character flaw. We study the impact of fallacies in online discussions and show the need
for improving tools for their detection. In addition, we look into detecting verifiable claims made by
politicians. We started a collaboration with RadioFrance and with Wikidébats, a debate platform focused
on proving quality arguments for controversial topics.

3.6 Measuring and mitigating risks of AI-driven information targeting

We are witnessing a massive shift in the way people consume information. In the past, people had an
active role in selecting the news they read. More recently, the information started to appear on people’s
social media feeds as a byproduct of one’s social relations. We see a new shift brought by the emergence
of online advertising platforms where third parties can pay ad platforms to show specific information
to particular groups of people through paid targeted ads. AI-driven algorithms power these targeting

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Inria Annual Report 2023

technologies. Our goal is to study the risks with AI-driven information targeting at three levels: (1) human-
level–in which conditions targeted information can influence an individual’s beliefs; (2) algorithmic-
level–in which conditions AI-driven targeting algorithms can exploit people’s vulnerabilities; and (3)
platform- level–are targeting technologies leading to biases in the quality of information different groups
of people receive and assimilate. Then, we will use this understanding to propose protection mechanisms
for platforms, regulators, and users.

4 Application domains

4.1 Cloud computing

Cloud computing services are strongly developing and more and more companies and institutions resort
to running their computations in the cloud, in order to avoid the hassle of running their own infrastructure.
Today’s cloud service providers guarantee machine availabilities in their Service Level Agreement (SLA),
without any guarantees on performance measures according to a specific cost budget. Running analytics
on big data systems require the user not to only reserve the suitable cloud instances over which the big
data system will be running, but also setting many system parameters like the degree of parallelism and
granularity of scheduling. Chosing values for these parameters, and chosing cloud instances need to
meet user objectives regarding latency, throughput and cost measures, which is a complex task if it’s done
manually by the user. Hence, we need need to transform cloud service models from availabily to user
performance objective rises and leads to the problem of multi-objective optimization. Research carried
out in the team within the ERC project “Big and Fast Data Analytics” aims to develop a novel optimization
framework for providing guarantees on the performance while controlling the cost of data processing in
the cloud.

4.2 Computational journalism

Modern journalism increasingly relies on content management technologies in order to represent,
store, and query source data and media objects themselves. Writing news articles increasingly requires
consulting several sources, interpreting their findings in context, and crossing links between related
sources of information. CEDAR research results directly applicable to this area provide techniques and
tools for rich Web content warehouse management. Within the SourcesSay AI Chair project, we work
to devise concrete algorithms and platforms to help journalists perform their work better and/or faster.
This work is in collaboration with the journalists from RadioFrance, the team Le vrai du faux.

4.3 Computational social science

Political discussions revolve around ideological conflicts that often split the audience into two opposing
parties. Both parties try to win the argument by bringing forward information. However, often this
information is misleading, and its dissemination employs propaganda techniques. We investigate the
impact of propaganda in online forums and we study a particular type of propagandist content, the
fallacious argument. We show that identifying such arguments remains a difficult task, but one of high
importance because of the pervasiveness of this type of discourse. We also explore trends around the
diffusion and consumption of propaganda and how this can impact or be a reflection of society.

4.4 Online targeted advertising

The enormous financial success of online advertising platforms is partially due to the precise targeting
features they offer. Ad platforms collect large amounts of data on users and use powerful AI-driven
algorithms to infer users’ fine-grain interests and demographics, which they make available to advertisers
to target users. For instance, advertisers can target groups of users as small as tens or hundreds and as
specific as “people interested in anti-abortion movements that have a particular education level”. Ad
platforms also employ AI-driven targeting algorithms to predict how “relevant” ads are to particular
groups of people to decide to whom to deliver them. While these targeting technologies are creating
opportunities for businesses to reach interested parties and lead to economic growth, they also open the

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way for interested groups to use user’s data to manipulate them by targeting messages that resonate with
each user.

5 Social and environmental responsibility

Madhulika Mohanty co-lead the SCOUT action of the Diversity, Equity and Inclusion initiative (website)
for the DB research community. This action was in-charge of writing the Code of Ethics and organization
guidelines to be used in future DB conferences. This has lead to the publication of [12].

5.1 Impact of research results

Our work on Big Data and AI techniques applied to data journalism and fact-checking have attracted
attention beyond our community and was disseminated in general-audience settings, for instance
through I. Manolescu’s participation in panels at Médias en Seine, at the Colloque Morgenstern at Inria
Sophia, and through invited keynotes, e.g., at DEBS 2022 and DASFAA 2022.

Our work in the SourcesSay project (Section 8.1.1), on propaganda detection (Section 8.4.1), and on
ad transparency (Section 8.2), goes towards making information sharing on the Web more transparent
and more trustworthy.

6 Highlights of the year

6.1 Awards

Oana Goga received the Lovelace-Babbage Award from the French Academy of Science and the French
Computer Society. It is given out annually to two researchers below the age of 40 that made significant
contributions to Computer Science, from the most theoretical to the most applied.

6.2 Engagement with journalists

In the spring, a journalist and data scientist, Camille Pettineo, worked in the team. Discussing with her,
we have devised new, more user-friendly interfaces for our software ConnectionLens, leading to the
ConnectionStudio system.

In May 2023, O. Balalau, N. Barret, S. Ebel, T. Galizzi and I. Manolescu presented tools developed within
the team (StatCheck, ConnectionStudio, and Abstra) to a meet-up of DataJournos, a data journalism
association. Approximately 40 people attended our presentation. This has lead to new collaborations
between O. Balalau and ADEME/AEF, on the topic of automatically detecting greenwashing through
Natural Language analysis.

In July 2023, N. Barret and M. Mohanty gave a tutorial on ConnectionStudio and how to use it for data
journalism, at the “Forum Medias et Développement” organised by CFI, the french media development
agency.

In September 2023, O. Balalau, T. Bouganim and I. Manolescu participated to SciCar ("Where Science
meets Computer-Assisted Reporting") conference in Dortmund, Germany. We presented our collabora-
tion with S. Horel (Le Monde).

In October 2023, O. Balalau, S. Ebel et T. Galizzi presented StatCheck, our fact-checking tool, at an IA

day organized by RadioFrance.

In November 2023, I. Manolescu participated in a debate on “New (AI) tools for media” at the “Médias

en Seine” journalism conference.

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Inria Annual Report 2023

7 New software, platforms, open data

7.1 New software

7.1.1 ConnectionLens

Name: Integration of heterogeneous data using information extraction

Keyword: Data analysis

Functional Description: ConnectionLens treats a set of heterogeneous, independently authored data
sources as a single virtual graph, whereas nodes represent fine-granularity data items (relational
tuples, attributes, key-value pairs, RDF, JSON or XML nodes. . . ) and edges correspond either to
structural connections (e.g., a tuple is in a database, an attribute is in a tuple, a JSON node has a
parent. . . ) or to similarity (sameAs) links. To further enrich the content journalists work with, we
also apply entity extraction which enables to detect the people, organizations etc. mentioned in
text, whether full-text or text snippets found e.g. in RDF or XML. ConnectionLens is thus capable of
finding and exploiting connections present across heterogeneous data sources without requiring
the user to specify any join predicate.

URL: https://team.inria.fr/cedar/connectionlens/

Publications: hal-02934277, hal-02904797, hal-01841009

Authors: Tayeb Merabti, Helena Galhardas, Julien Leblay, Ioana Manolescu, Oana-Denisa Balalau, Catar-

ina Pinto Conceicao, Madhulika Mohanty

Contact: Manolescu Ioana

7.1.2 Abstra

Name: Abstra: Toward Generic Abstractions for Data of Any Model

Keywords: Heterogeneous Data, Data Exploration, Data analysis, Databases, LOD - Linked open data

Functional Description: Abstra computes a description meant for humans, based on the idea that,
regardless of the syntax or the data model, any dataset holds some collections of entities/records,
that are possibly linked with relationships. Abstra relies on a common graph representation of any
incoming dataset, it leverages Information Extraction to detect what the dataset is about, and relies
on an original algorithm for selecting the core entity collections and their relations. Abstractions
are shown both as HTML text and a lightweight Entity-Relationship diagram. A GUI also allows to
tune the abstraction parameters and explore the dataset.

URL: https://team.inria.fr/cedar/projects/abstra/

Contact: Nelly Barret

7.1.3 StatCheck

Name: Fact-checking Multidimensional Statistic Claims in French

Keywords: Machine learning, Databases, Natural language processing, Software engineering

Scientific Description: To strengthen public trust and counter disinformation, computational fact-
checking, leveraging digital data sources, attracts interest from the journalists and the computer
science community. A particular class of interesting data sources comprises statistics, that is,
numerical data compiled mostly by governments, administrations, and international organizations.
Statistics are often multidimensional datasets, where multiple dimensions characterize one value,
and the dimensions may be organized in hierarchies. This paper describes STATCHECK, a statistic
fact-checking system jointly developed by the authors, which are either computer science research-
ers or fact-checking journalists working for a French-language media with a daily audience of more

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than 15 millions (aud, 2022). The technical novelty of STATCHECK is twofold: (i) we focus on multi-
dimensional, complex-structure statistics, which have received little attention so far, despite their
practical importance, and (ii) novel statistical claim extraction modules for French, an area where
few resources exist. We validate the efficiency and quality of our system on large statistic datasets
(hundreds of millions of facts), including the complete INSEE (French) and Eurostat (European
Union) datasets, as well as French presidential election debates.

Functional Description: StatCheck firstly allows the collection of data for its operation. Two types of
data are collected: statistical tables and posts from social networks: - Acquisition of statistical files
on the site of referent organisations (INSEE, Eurostat) - Extraction of statistical tables from these
files, and storage of the extracted tables - Acquisition of political tweets from a list of accounts
The application allows the detection, extraction and search of statistical facts: - Detection and
extraction of statistical facts from Twitter posts (e.g. "Unemployment rate increased by 30% in
2023) - Search for statistical facts in our database. Display of the twenty most relevant statistical
tables for a statistical fact - Automatic transcription of audio files to detect and extract transcripts
of statistical facts.

Release Contributions: - Redesign of the user interface - Modification of the software architecture -

Addition of audio transcription

URL: https://cedar-rf.saclay.inria.fr/

Publications: hal-01496700, hal-01745768, hal-02121389, hal-01915148, hal-03767992, hal-03791175

Contact: Ioana Manolescu

Participants: Tien Duc Cao, Ioana Manolescu, Xavier Tannier, Oana-Denisa Balalau, Simon Ebel, Theo

Galizzi

7.1.4 ConnectionStudio

Keywords: Heterogeneous Data, Data Exploration

Functional Description: ConnectionStudio integrates highly heterogeneous data into graphs, enriched
with extracted entities. Studio users can discover the entities in their data, navigate across connec-
tions between datasets, explore and query the data in many ways. The Studio currently supports:
CSV, JSON, XML, RDF, text, property graphs, all Office formats, and PDF datasets.

ConnectionStudio is a novel front-end to ConnectionLens, Abstra and PathWays (see also the
respective Web sites). Its own novel features are outlined in a CoopIS 2023 article.

URL: https://connectionstudio.inria.fr/

Contact: Ioana Manolescu

7.1.5 FactSpotter

Keywords: Factual Faithfulness, Text generation

Functional Description: We propose a new metric that correctly identifies factual faithfulness, i.e., given
a triple (subject, predicate, object), it decides if the triple is present in a generated text. We show that
our metric FactSpotter achieves the highest correlation with human annotations on data correct-
ness, data coverage, and relevance. In addition, FactSpotter can be used as a plug-in feature to
improve the factual faithfulness of existing models.

Contact: Kun Zhang

Partner: Ecole Polytechnique

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7.1.6 PathWays

Inria Annual Report 2023

Keywords: Named entities, Data Journalism

Functional Description: PathWays is a system that is able to find connections between entities, such as
people, organizations and locations, across documents. For this task, PathWays starts by loading a
set of (potentially heterogeneous) datasets as a data graph (see https://arxiv.org/abs/2012.08830)
and then builds a collection graph, i.e. a summary of it (see https://hal.inria.fr/hal-03767967).
Based on that collection graph, PathWays enumerates all paths connecting two user-specified
entities.

URL: https://team.inria.fr/cedar/projects/pathways/

Contact: Nelly Barret

7.1.7 OpenIEEntity

Name: Open Information Extraction with Entity Focused Constraints

Keyword: Information extraction

Functional Description: This tool takes in input a sentence and outputs the facts contained in the

sentence, in the format (subject,predicate,object).

Contact: Oana-Denisa Balalau

8 New results

8.1 Data management for analyzing digital arenas

8.1.1 Graph integration of heterogeneous data sources for data journalism

Work carried within the ANR AI Chair SourcesSay project has focused on developing a platform for
integrating arbitrary heterogeneous data into a graph, then exploring and querying that graph in a simple,
intuitive manner through keyword search. The main technical challenges are: (i) how to interconnect
structured and semi-structured data sources? We address this through information extraction (when
an entity appears in two data sources or two places in the same graph, we only create one node, thus
interlinking the two locations) and through similarity comparisons; (ii) how to find all connections
between nodes matching specific search criteria, or certain keywords? The question is particularly
challenging in our context since ConnectionLens graphs can be pretty large, and query answers can
traverse edges in both directions.

In this context, the following new contributions have been brought:

1. ConnectionStudio: a user-friendly data lake for data exploration. ConnectionStudio [17, 18, 19]
is a user-friendly data lake, ingesting structured, semi-structured and un-structured documents
(XML, JSON, CSV, RDF, PG, PDFs and Office files). It provides users different ways to explore and
query the underlying data. For instance, it provides a set of lake-level statistics about the entities
found in the data and Entity-Relationship diagrams showing the datasets structures. Further, it
allows users to enumerate and browse a set of (interesting) paths connecting entities of interest in
the data. It also provides a querying interface to let users formulate their queries using few variables
and inspect the data lake without having to write SQL queries. Finally, users can save, export and
share results they produce in the lake, e.g., data journalists can share them in newsrooms.

2. Integrating Connection Search in Graph Queries. When graph database users explore unfamiliar
graphs, potentially with heterogeneous structure, users may need to find how two or more groups
of nodes are connected in a graph, even when users are not able to describe the connections. This
is only partially supported by existing query languages, which allow searching for paths, but not for
trees connecting three or more node groups. In this work, we formally showed how to integrate

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connecting tree patterns (CTPs, in short) with a graph query language such as GPML, SPARQL
or Cypher, leading to Extended Queries (or EQs, in short). We then study a set of algorithms for
evaluating CTPs; we generalize prior keyword search work to be complete, most importantly by (i)
considering bidirectional edge traversal, (ii) allowing users to select any score function for ranking
CTP results and (iii) returning all results. To cope with very large search spaces, we propose efficient
pruning techniques and formally establish a large set of cases where our best algorithm, MOLESP,
is complete even with pruning. Our experiments validate the performance of our algorithms on
many synthetic and real-world workloads [15, 16, 23].

3. Big Graph visualization and exploration. In this project, we focus on understanding the role
played by each node in a graph and building a visualisation tool to explore it node by node, showing
the most pertinent next neighbour to visit for a node. This visualisation can be plugged in the the
tools we develop in our team. Our challenges are the following. First, nodes in a graph can play
several roles, they can be part of an entity, being the root of the entity or describing the entity, such
as attributes or values, or they can take part in relations between entities. We have identified and
attributed automatically the roles played in a graph by each node. Second, when a user wants to
explore the neighbour of a node, we should suggest relevant neighbours. We are working on node
scoring functions to determine which neighbours of a node are the most relevant to explore next.

ConnectionLens is available online at: ConnectionLens Gitlab repository, while ConnectionStudio is

available at ConnectionStudio Gitlab repository.

8.1.2 Fact-checking Multidimensional Statistic Claims in French

To strengthen public trust and counter disinformation, computational fact-checking, leveraging digital
data sources, attracts interest from journalists and the computer science community. A particular class of
interesting data sources comprises statistics, that is, numerical data compiled mostly by governments,
administrations, and international organizations. Statistics are often multidimensional datasets, where
multiple dimensions characterize one value and the dimensions may be organized in hierarchies. To
address this challenge we developed STATCHECK, a statistic fact-checking system, in collaboration with
RadioFrance. The initial technical novelties of STATCHECK were twofold: (i) we focus on multidimen-
sional, complex-structure statistics, which have received little attention so far, despite their practical
importance; and (ii) novel statistical claim extraction modules for French, an area where few resources
exist. We validate the efficiency and quality of our system on large statistic datasets (hundreds of millions
of facts), including the complete INSEE (French) and Eurostat (European Union) datasets, as well as
French presidential election debates [2]. In 2023, we have further improved the plaform with: (i) the use
of LLM for both the table extraction and the table retrieval; (ii) extending the list of data sources, from
which we gather tables, in a extensible and dynamic manner (Focus crawling, incremental crawler) (iii)
automatical transcription and analysis of audio files, this new capatibility is perticularly important in the
collaboration with Franceinfo, since journalists work a lot with radio feeds; (iv) detection and analysis of
propaganda.

8.2 Online targeted advertising

8.2.1 On Detecting Policy-Related Political Ads: An Exploratory Analysis of Meta Ads during the 2022

French Election

Online political advertising has become the cornerstone of political campaigns. The budget spent solely
on political advertising in the U.S. has increased by more than 1002017-2018 U.S. election cycle to 1.6
billion during the 2020 U.S. presidential elections. Naturally, the capacity offered by online platforms to
micro-target ads with political content has been worrying lawmakers, journalists, and online platforms,
especially after the 2016 U.S. presidential election, where Cambridge Analytica has targeted voters with
political ads congruent with their personality. To curb such risks, both online platforms and regulators
(through the DSA act proposed by the EuropeanCommission) have agreed that researchers, journal-
ists, and civil society need to be able to scrutinize the political ads running on large online platforms.
Consequently, online platforms such as Meta and Google have implemented Ad Libraries that contain

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information about all political ads running on their platforms. This is the first step on a long path. Due
to the volume of available data, it is impossible to go through these ads manually, and we now need
automated methods and tools to assist in the scrutiny of political ads. In this work [25], we focus on
political ads that are related to policy. Understanding which policies politicians or organizations promote
and to whom is essential in determining dishonest representations. This paper proposes automated
methods based on pre-trained models to classify ads in 14 main policy groups identified by the Com-
parative Agenda Project (CAP). We discuss several inherent challenges that arise. Finally, we analyze
policy-related ads featured on Meta platforms during the 2022 French presidential elections period.

8.2.2 Collaborative Ad Transparency: Promises and Limitations

Several targeted advertising platforms offer transparency mechanisms, but researchers and civil societies
repeatedly showed that those have major limitations. In this work [22], we propose a collaborative ad
transparency method to infer, without the cooperation of ad platforms, the targeting parameters used
by advertisers to target their ads. Our idea is to ask users to donate data about their attributes and the
ads they receive and to use this data to infer the targeting attributes of an ad campaign. We propose
a Maximum Likelihood Estimator based on a simplified Bernoulli ad delivery model. We first test our
inference method through controlled ad experiments on Facebook. Then, to further investigate the
potential and limitations of collaborative ad transparency, we propose a simulation framework that
allows varying key parameters. We validate that our framework gives accuracies consistent with real-
world observations such that the insights from our simulations are transferable to the real world. We then
perform an extensive simulation study for ad campaigns that target a combination of two attributes. Our
results show that we can obtain good accuracy whenever at least ten monitored users receive an ad. This
usually requires a few thousand monitored users, regardless of population size. Our simulation framework
is based on a new method to generate a synthetic population with statistical properties resembling the
actual population, which may be of independent interest.

8.2.3 Marketing to Children Through Online Targeted Advertising: Targeting Mechanisms and Legal

Aspects

Many researchers and organizations, such as WHO and UNICEF, have raised awareness of the dangers of
advertisements targeted at children. While most existing laws only regulate ads on televi- sion that may
reach children, lawmakers have been working on extending regulations to online advertising and, for
example, forbid (e.g., the DSA) or restrict (e.g., the COPPA) advertising based on profiling to children. At
first sight, ad platforms such as Google seem to protect chil- dren by not allowing advertisers to target
their ads to users that are less than 18 years old. However, this work [24] shows that other targeting
features can be exploited to reach children. For example, on YouTube, advertisers can target their ads to
users watching a particular video through placement-based targeting, a form of con- textual targeting.
Hence, advertisers can target children by simply placing their ads in children-focused videos. Through a
series of ad experiments, we show that placement-based targeting is possible on children-focused videos
and, hence, enables marketing to chil- dren. In addition, our ad experiments show that advertisers can
use targeting based on profiling (e.g., interest, location, behavior) in combination with placement-based
advertising on children-focused videos. We discuss the lawfulness of these two practices with re- spect to
DSA and COPPA. Finally, we investigate to which extent real-world advertisers are employing placement-
based targeting to reach children with ads on YouTube. We propose a measurement methodology
consisting of building a Chrome extension able to capture ads and instrumenting six browser profiles to
watch children-focused videos. Our results show that 7we test use placement-based targeting. Hence,
targeting children with ads on YouTube is not only hypothetically possible but also occurs in practice.
We believe that the current legal and technical solutions are not enough to protect children from harm
due to online advertising. A straightforward solution would be to forbid placement-based advertising on
children-focused content.

8.2.4 Understanding the privacy risks of popular search engine advertising systems

Privacy-focused search engines such as DuckDuckGo, StartPage, and Qwant promote a strategy of
respecting users’ privacy and promise not to track users’ search and browsing behavior. However, they rely

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on advertising for revenue, using Microsoft’s (DuckDuckGo and Qwant) or Google’s (StartPage) advertising
systems. Moreover, these search engines are often silent or ambiguous on the privacy properties of the
ads that appear on their search page. Our research [21] delves into the privacy properties of advertising
systems used by these search engines. Our findings reveal that the privacy protections of private search
engines do not sufficiently cover their advertising systems. Although these search engines refrain from
identifying and tracking users and their ad clicks, the presence of ads from Google or Microsoft subjects
users to the privacy-invasive practices performed by these two advertising platforms. When users click
on ads on private search engines, they are often identified and tracked either by Google, Microsoft, or
other third parties, through bounce tracking and UID smuggling techniques.

8.3 How effective are tracking restrictions: A case study on Meta

Growing concern over digital privacy has led to the widespread use of tracking restriction tools such as
ad blockers, Virtual Private Networks (VPN), and privacy-focused web browsers. Despite these efforts,
advertising companies continuously innovate to overcome these restrictions. Recently, advertising
platforms like Meta have been promoting server-side tracking solutions to bypass traditional browser-
based tracking restrictions.

We explore how server-side tracking technologies can link website visitors with their user accounts on
Meta products. The goal is to assess the effectiveness and accuracy of employing this technology, as well
as the effect of tracking restrictions on online tracking. Our methodology involves a series of experiments
where we integrate Meta’s client-side tracker (the Meta Pixel) and server-side technology (the Conversions
API) on different web pages. We then drive traffic to these pages and evaluate the success rate of linking
website visitors to their profiles on Meta products.

8.4 Improving the quality of public debate with AI

8.4.1 Argumentation mining and applications to public discourse

Humans use argumentation daily to evaluate the validity of new ideas, convince an addressee, or solve
a difference of opinion. An argument contains a statement to be validated (a proposition also called
claim or conclusion), a set of backing propositions (called premises, which should be accepted ideas),
and a logical connection between all the pieces of information presented that allows the inference of the
conclusion. In this work, we will focus on fallacies: weak arguments that seem convincing, however, their
evidence does not prove or disprove the argument’s conclusion. Fallacy detection is part of argumentation
mining, the area of natural language processing dedicated to extracting, summarizing, and reasoning
over human arguments. The task is closely related to propaganda detection, where propaganda consists
of a set of manipulative techniques, such as fallacies, used in a political context to enforce an agenda [3].
In the past, we have worked on propaganda [3] and fallacy detection [11]. We continue this work with a
CIFRE PhD, a collaboration between the Amundi company, Inria and Télécom Paris. This thesis aims
to improve fallacy detection in natural language by leveraging both language patterns and additional
information, such as common sense knowledge, encyclopedic knowledge and logical rules. To achieve
this we will focus on how fallacies can be represented and how we can classify reasoning patterns in
argumentation. The interest of Amundi is in how argumentation can be applied to finding examples of
greenwashing. We are currently collaborating with ADEME and AEF Info on the topic of greenwashing
detection.

8.4.2 Finding conflicts of interest via open information extraction with entity-focused constraints

Open Information Extraction (OIE) is the task of extracting tuples of the form (subject, predicate, object),
without any knowledge of the type and lexical form of the predicate, the subject, or the object. In this
work, we focus on improving OIE quality by exploiting domain knowledge about the subject and object.
More precisely, knowing that the subjects and objects in sentences are often named entities, we explore
how to inject constraints in the extraction through constrained inference and constraint-aware training.
An important use case that we want to pursue next is automatically creating a knowledge base of relations
between scientists and companies, i.e. identifying conflict-of-interest between the scientists and funding
bodies, where the named entities are the names of scientists and companies, and the relation describes

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Inria Annual Report 2023

the conflict of interest between them. Our work [26] leverages the state-of-the-art OpenIE6 platform,
which we adapt to our setting. Through a carefully constructed training dataset and constrained training,
we obtain a 29.17

8.4.3 Detecting hallucinations in Large Language Models

This research [27] investigates the extent to which the Graph-to-Text (G2T) generation problem is ad-
dressed in existing datasets and the performance of metrics in text comparison. A key focus is on a
new metric, FactSpotter, developed to assess factual faithfulness in generated texts from G2T models.
FactSpotter is shown to correlate highly with human annotations in terms of data correctness, coverage,
and relevance. It functions as a plugin feature to enhance the factual accuracy of existing models and
examines the current challenges in G2T datasets.

FactSpotter, initially developed for evaluating the factual faithfulness of Graph-to-Text generation,
has its potential for expansion into a more general text similarity metric. This would enable it to assess a
wider range of text generation tasks, extending its applicability to various narrative forms and data-driven
journalism.

8.5 Efficient Big Data analytics

8.5.1 Efficient and robust active learning methods for interactive database exploration

There is an increasing gap between fast growth of data and the limited human ability to comprehend
data. Consequently, there has been a growing demand of data management tools that can bridge this
gap and help the user retrieve high-value content from data more effectively. In this work [14], we
propose an interactive data exploration system as a new database service, using an approach called
"explore-by-example." Our new system is designed to assist the user in performing highly effective data
exploration while reducing the human effort in the process. We cast the explore-by-example problem
in a principled "active learning" framework. However, traditional active learning suffers from two
fundamental limitations: slow convergence and lack of robustness under label noise. To overcome the
slow convergence and label noise problems, we bring the properties of important classes of database
queries to bear on the design of new algorithms and optimizations for active learning-based database
exploration. Evaluation results using real-world datasets and user interest patterns show that our new
system, both in the noise-free case and in the label noise case, significantly outperforms state-of-the-art
active learning techniques and data exploration systems in accuracy while achieving the desired efficiency
for interactive data exploration.

8.5.2 Multi-Objective Optimization for Spark-based Data Analytics

Spark has been widely used for data analytics in the cloud. Determining an optimal configuration of a
Spark physical plan based on user-specified objectives is a complex task. It is challenging from three
aspects. Firstly, a Spark physical plan, or query, can be represented as a Directed Acyclic Graph (DAG)
of “query stages,” where parameters of each stage are controlled under the granularity of the query (i.e.
Spark-context parameters, e.g. resources are shared among all stages) and the granularity of the stage (i.e.
different among different stages). The correlation of parameters under multiple granularity makes the
performance tuning of a query more complicated. Secondly, the parameters of each stage face timing
constraints. Spark-context parameters should be set at compile time and cannot change during runtime,
while stage-level parameters can be modified during runtime. Thirdly, Multi-Objective Optimization
(MOO) is necessary when there are multiple potentially conflicting, user performance objectives such as
latency and cost. This work focuses on the algorithm design to return Pareto optimal configurations for
a query with parameters under multi-granularity control and different timing constraints. It captures
tradeoffs among various objectives and recommends an optimal configuration based on user preferences.
The expectation is to provide recommendations for all stages in a query within a few seconds.

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8.5.3 Scalable Analytics on Multi-Streams Dynamic Graphs

Several real-time applications rely on dynamic graphs to model and store data arriving from multiple
streams. In addition to the high ingestion rate, the storage and query execution challenges are ampli-
fied in contexts where consistency should be considered when storing and querying the data. In this
project [28], we addresses the challenges associated with multi-stream dynamic graph analytics. We
propose a database design that can provide scalable storage and indexing, to support consistent read-only
analytical queries (present and historical), in the presence of real-time dynamic graph updates that arrive
continuously from multiple streams.

8.6 Explainable Anomaly Detection on Multivariate Time Series in the AIOps Do-

main

The widespread adoption of Internet-based services by software companies, as well as the scale and
complexity at which they operate, have made incidents in their IT operations increasingly more likely,
diverse and impactful. This has led to the rapid development of a central aspect of the "Artificial Intel-
ligence for IT Operations" (AIOps) domain, focusing on detecting abnormal patterns in vast amounts
of multivariate time series (MTS) data generated by service entities. Although numerous MTS anomaly
detection methods have been developed, the state of the art still presents some limitations due to the
unique challenges posed by AIOps. These challenges include 1) the presence of complex, noisy and di-
verse normal behaviors, 2) the wide variety of anomaly types and difficulty in providing detailed anomaly
labels, and 3) the need to generalize to a wide variety of normal behaviors for the monitored entities.

Our research focused on designing new anomaly detection methods to address these AIOps challenges,
mainly through explicit context generalization and weak supervision, as well as conducting thorough
experiments to demonstrate their superiority. Concurrently, we continued developing our data science
pipeline for explainable anomaly detection over MTS, to further facilitate the design and benchmarking
of new techniques for the community.

9 Partnerships and cooperations

9.1 European initiatives

9.1.1 Horizon Europe

Oana Goga is the PI – ERC Starting Grant 2022 – 2027 “MOMENTOUS: Measuring and Mitigating Risks of
AI-driven Information Targeting” (1,499,952 €)

Ioana Manolescu is the local PI for the Inria partner in the project "ELIAS - European Lighthouse of AI

for Sustainability" (2,800,000€). Oana Goga is also involved.

9.1.2 H2020 projects

Oana Goga is the local PI for CNRS partner – EU H2020 2021 – 2024 “Trust aWARE: Enhancing Digital
Security, Privacy and TRUST in softWARE” (our part: 461,000 €)

9.2 National initiatives

9.2.1 ANR

• Oana Goga is the local PI for LIX partner – ANR PRC 2022 – 2026 “FeedingBias: A multi-platform

mixed-methods approach to news exposure on social media” (our part: 128,000 €)

• Oana Goga is the local PI for LIX partner – ANR PRCE 2021 – 2025 “PROPEOS: Privacy-oriented

Personalization of Online Services” (our part: 202,720 €)

• Ioana Manolescu is the local PI for Inria Saclay in CQFD (2019-2024), an ANR project coordinated
by F. Ulliana (U. Montpellier). Its research aims at investigating efficient data management methods
for ontology-based access to heterogeneous databases (polystores).

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Inria Annual Report 2023

• Ioana Manolescu is the PI of SourcesSay (2020-2024), an AI Chair funded by Agence Nationale
de la Recherche and Direction Générale de l’Armement. The project goal is to interconnect data
sources of any nature within digital arenas. In an arena, a dataset is stored, analyzed, enriched and
connected, graph mining, machine learning, and visualization techniques, to build powerful data
analysis tools.

9.2.2 Others

• ANRT Project: CIFRE Amundi, advised by Oana Balalau and F.Suchanek (Télécom Paris). The goal of
this thesis is to improve fallacy detection in natural language, by leveraging both language patterns
but also additional information, such as common sense knowledge, encyclopedic knowledge and
logical rules. To achieve this we will focus on how fallacies can be represented1 and how we can
classify reasoning patterns in argumentation.

9.3 Regional initiatives

Ioana Manolescu is the PI for the region-funded project DIM AI4IDF. 2023 has been its third and last year;
in particular, it has supported the PhD of Nelly Barret.

Ioana Manolescu, Oana Balalau have an Action Exploratoire Inria project (2023-2025), "JoDaIA:

Harnessing Data and AI for Journalism".

Hi!Paris Collaborative Project (2022-2024) coordinated by Oana Balalau and J.Romero (Télécom

SudParis).

10 Dissemination

10.1 Promoting scientific activities

10.1.1 Scientific events: organisation

General chair, scientific chair Oana Balalau and Ioana Manolescu have been involved in organizing the
Hi!Paris summer school in AI for Science and Society, in July 2023, at HEC, Jouy en Josas. Ioana Manolescu
has co-chaired the school, and Oana Balalau has been a session chair.

Member of the organizing committees

• Ioana Manolescu has co-organized the VLDB Summer School in Data Management in Cluj, Ro-

mania, in July 2023.

• Oana Goga and Ioana Manolescu were co-organizers of the first interdisciplinary workshop "Infox
sur Seine", bringing social scientists, political scientists and computer scientists together around
topics of dis- and mis-information, March 22-23, 2023.

• Yanlei Diao was the organizer of the ERC BigFastData workshop, a two day event at Ecole Polytech-

nique, October 5-6, 2023, with invited speakers all over Europe and USA.

10.1.2 Scientific events: selection

Chair of conference program committees
ence, Meta-reviewer (Industry and Applications track) at the IEEE ICDE 2023 conference

Ioana Manolescu: Tutorial chair at the EDBT 2023 Confer-

Member of the conference program committees

• Oana Balalau: ACL 2023, EMNLP 2023, CSCW 2024, BDA2023

• Oana Goga: The Web Conference (2023), USENIX Security (2023), ConPro (2023), FAccT (2023),

IC2S2 (2023)

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• Ioana Manolescu: ACM SIGMOD 2023, BDA (Demonstrations) 2023

• Madhulika Mohanty: CoDS-COMAD 2023, WSDM 2023, SIGMOD Availability and Reproducibility

2023, DSAA 2023, SIGIR (Demo) 2023, ICWE 2023, IJCAI 2023, AIMLSystems 2023.

• Kun Zhang: ICANN 2023

10.1.3 Journal

Member of the editorial boards
September 2023

Ioana Manolescu joined the editorial board of the VLDB Journal in

Reviewer - reviewing activities Madhulika Mohanty: IEEE Transactions on Knowledge and Data Engin-
eering (TKDE), Knowledge and Information Systems (KAIS)

10.1.4 Invited talks

• Ioana Manolescu:

– Keynote “All your digital data sources in one place: the SourcesSay ANR/DGA project” at the

Infox sur Seine workshop, on March 22, 2023

– Keynote at the yearly meeting of the "RADIA (Raisonnement, Données et IA)" CNRS research

group, in Strasbourg, on July 2, 2023.

• Oana Balalau, Théo Bouganim and Ioana Manolescu were invited to present, together with Stéphane
Horel (Le Monde) and Gary Fooks and Tom Mills (UK), our joint work on use ConnectionLens to
build a database about conflicts of interest in the biomedical domain, at SciCAR 2023 (Sept 29-30,
2023, in Dortmund, Germany).

• Oana Balalau presented her work at AIvolution, a workshop on AI and digital technologies at the

European Parliment in Brussels.

• Oana Goga:

– (N) Musée des Arts et Métiers – L’aventure des inventions, Feb 2024, general audience, invited

along very distinguished speakers (I) WSTEAM Panel AICCSA, Dec 2023 – non research

– (I) Plenary talk for AIvolution, European Parliament, Bruxelles, November 2023

– (I) Plenary talk at the OECD event Tackling disinformation: Strengthening democracy through
information integrity, OECD, Paris, November 2023, 400+ participants, I presented along
government officials with high functions such as chancellors and state secretaries

– (I) Plenary talk at Atelier 2 - Projet de recherche collective - Vers un droit neuroéthique?, Paris,

November 2023

– (I) Keynote at Journées Nationales GDR Securité, Paris, June 2023

– (F) Plenary talk at VIGINUM on “Rencontres et débats autour des manipulations de l’information”,

Paris, June 2023

– (F) Plenary talk at Conférence Arcom x CNNum on “Role de la société civile dans la régulation

numérique nationale et européenne”, Paris, June 2023

– (I) Keynote at 7th ASF/RSD Winter school, Sept Laux, January 2023

– (N) Seminar DataScale master, Versailles, Dec 2023

– (N) LIX Lab Seminar, Palaiseau, April 2023

– (N) Inria PETRUS Team Seminar, Versailles, April 2023

– (N) PEREN–Pole d’Expertise de la Regulation Numerique (Academic Seminar), Quai de
Bercy, Paris, March 2023 (N) Université Dauphine (Seminar “Responsabilité Sociale des Al-
gorithmes”), Paris, February 2023

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Inria Annual Report 2023

• Yanlei Diao:

– SWIFT Global AI Forum, February 8th, 2023

– Database Summit, MIT, October 20, 2023

10.1.5 Leadership within the scientific community

• Ioana Manolescu became the president of the steering committee of the BDA association "Commun-
auté Francophone en Gestion de données : Principes, Technologies et Applications" in November
2023.

• Yanlei Diao served as a member of ACM SIGMOD Award Committee

10.1.6 Scientific expertise

• Oana Goga is working as an External Expert with contract with the European Commission (DG
Connect and ECAT), to advise on the Delegated Act addressing the implementation of the DSA
Article 40 that gives vetted researchers access to data from very large online platforms and very
large search engines.

• Oana Goga became a member of Conseil Scientifique of Regalia (2023)

• Oana Goga became a member of the Science Advisory Committee for the NSF-funded Mid-scale
RI-1 project (a 15 million US project): Observatory for online human and platform behavior (2022-
2023).

• June 2022, Oana Goga was part of a team that answered the ARCOM “Public consultation on access
to data from online platforms”. The new Digital Service Act provides legal grounds for researchers
to ask for data from online platforms. The ARCOM proposed several procedures to access this
data. We wrote a document highlighting the problems with the current procedures. The fact
that together with Beatrice Roussillon and Juliette Senechal we organized a community of social
scientists, computer scientists, and legal scholars around online platforms allowed us to be in the
perfect position to provide clear and informed answers to the consultation.

10.1.7 Research administration

• Ioana Manolescuis a member of the Comité de Direction of LIX, the Computer Science lab of Ecole

Polytechnique to which the team belongs.

• Oana Goga is a co-leader of the Action PLATFORM in GDR CIS.

10.2 Teaching - Supervision - Juries

10.2.1 Teaching

Oana Balalau is a part-time (33%) assistant professor at Ecole Polytechnique, where she teaches two
courses:

• INF473G “Mining, learning and reasoning on Web Graphs”, L3, Ecole Polytechnique

• INF583 “Systems for Big Data”, M1, Ecole Polytechnique

Nelly Barret: CSE204 (Machine Learning), Bachelor 2nd year, Ecole Polytechnique, 20h TP.
Salim Chouaki:

• Labs - Programming project in Python - ENSAE

• Labs - Programming in C++ - ENSAE

Yanlei Diao: Instructor, “Systems for Big Data”, M1, Ecole Polytechnique
Ioana Manolescu

Project CEDAR

19

• Part-time professor (50%) at Ecole Polytechnique. Courses, labs and TDs in INF553 (Database
Management Systems); in charge of the M1 Internship program in Artificial Intelligence and Data
Science (INF592); in charge of the Artificial Intelligence and Data Science program (M1) at Ecole
Polytechnique

• Taught TPT DATAIA921 (Architectures for Big Data), Institut Mines Télécom, 12h of lecture.

Madhulika Mohanty:

• TPT DATAIA921 (Architectures for Big Data), M2, Institut Mines Télécom, 9h TP.

• INF540 (Databases), M1MATHAPP, Ecole Polytechnique, 35h EQTD.

10.2.2 Supervision

PhD supervision: The team has supervised the following PhDs students:

• Nardjes AMIEUR, from February 2023 to December 2023 (Oana Goga)

• Nelly Barret, from January to December 2023 (Ioana Manolescu )

• Théo Bouganim, January to December 2023 (Ioana Manolescu, E. Pietriga (Inria ILDA))

• Tom Calamai, from January to December 2023 (Oana Balalau, F.Suchanek)

• Salim Chouaki, from January to December 2023 (Oana Goga)

• Asmaa EL FRAIHI, from February 2023 to December 2023 (Oana Goga)

• Qi Fan, from January to December 2023 (Yanlei Diao)

• Antoine Gauquier, from October to December 2023, at ENS Paris (Ioana Manolescu, Pierre Senellart)

• Ghufran Khan, from January to December 2023 (A. Anadiotis, Ioana Manolescu)

• Vincent Jacob, from January to December 2023 (Yanlei Diao)

• Chenghao Lyu, from January to December 2023 (Yanlei Diao, visiting PhD From UMass Amherst)

• Vera Sosnovik, at LIG, from January to July 2023 (Oana Goga)

• Kun Zhang, from September to December 2023 (Oana Balalau, Ioana Manolescu)

Engineers supervision:

• Tinhinane Medjkoune, at LIG, Jan. 2022 – Jan. 2023 (Oana Goga)

• Théo Galizzi, Simon Ebel (Ioana Manolescu, Oana Balalau )

• Arnab Singh (Yanlei Diao)

Intern supervision: The team has supervised the following interns:

• Abir Benzaamia (M2, ESI Alger) "Privacy-preserving recommender systems on mobile phones",

Oana Goga

• Nikola Dobricic (Bachelor 3A, Ecole Polytechnique) "User-friendly and expressive interfaces for

querying data in ConnectionStudio", Ioana Manolescu, Simon Ebel

• Melissa Mokhtari (M2, ESI Alger) "Studying how (mis)information spreads in Facebook groups",

Oana Goga

• Maria Osman-Calescu (Bachelor 3A, Ecole Polytechnique): "Deduplicating entities based on data

cleaning functions",Ioana Manolescu

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Inria Annual Report 2023

• Tu Nguyen (Bachelor 2A, Ecole Polytechnique): "Open information extraction with applicatins to

conflict of interest extraction", Oana Balalau, Ioana Manolescu, P. Upadhyay

Part-time project supervision The team has supervised the following part-time research projects:

• Junyuan Wang, Zhuoya Zang (L3, Ecole Polytechnique), "Mapping and analysing statistical sites",

Simon Ebel and Ioana Manolescu

• Isotta Magistrali, Gabriele Mura (L3, Ecole Polytechnique), "Analysing AI-automated fact-checks",

Théo Galizzi, Ioana Manolescu,Oana Balalau

• Tudor Enache (L3, Ecole Polytechnique), "From Simple Graphs to Property Graphs", Nelly Barret,

Madhulika Mohanty, Ioana Manolescu

• Shay Pripstein (L3, Ecole Polytechnique), "Targeted Data Acquisition from Open Data Repositories",

Madhulika Mohanty, Nelly Barret,Ioana Manolescu

• Andrei Bornea (L2, Ecole Polytechnique), "Open information extraction with constraints", Oana Balalau,

Julien Romero

10.2.3 Juries

• Ioana Manolescu has participated to a hiring committee for a Database/AI professor at Université

de Grenoble.

• Ioana Manolescu has been part of the PhD defense committee of Wafaa Al-Husaini, at Univ. Rennes

1. She has been a member of a comité de suivi of François Maine, at U. Paris Sorbonne.

• Oana Balalau and Yanlei Diao have been part of the PhD defense committee of Katia Antonenko,

Ecole Polytechnique. Yanlei Diao was the president of the jury.

• Oana Balalau has been a jury in the comité de suivi for: Rajaa El Hamdani (Télécom Paris), Cyril

Chhun (Télécom Paris), and Jonathan Colin (Université de Saclay)

• Oana Goga was in the following juries:

– Institute Evaluation Committee: Evaluation of the Max Planck Graduate Center Computer

Science (Jan 2023)

– Award Committee: CNIL-Inria Privacy Protection Award (2023),

– Ph.D. Committee: Moitree Basu (Jan 2024), Anne Josiane (May 2023) – as jury member

– Grants Commitees: ANR - Comité d’évaluation CE39 (2023), Max Planck Society Project (2023),

German National Research Center for Applied Cybersecurity ATHEN (2023)

10.3 Popularization

10.3.1 Internal or external Inria responsibilities

• Ioana Manolescu has been elected a member at the Inria Commission d’Evaluation in July 2023.

This newly elected commission started work in September 2023.

• Oana Balalau was a member of the GT comité de centre, Inria Saclay’s Scientific Commission, and

the comittee Moyens calcul Inria.

Project CEDAR

21

10.3.2 Articles and contents

• An interview with Ioana Manolescu on fact-checking and journalistic fact-checking appeared in
“20 minutes”: Le monde ne se privera jamais d’un journaliste qui s’y connaît dans un domaine,
pour Ioana Manolescu

• StatCheck, the statistic fact-checking tool we develop and which RadioFrance uses, has been
featured in “La revue des médias”, in an article by Xavier Eutrope: Intelligence artificielle et médias :
cinq utilisations, au-delà de ChatGPT

• Ioana Manolescu participated to a debate on “New (AI) tools for media” at the “Médias en Seine”
journalism conference in November 2023: Table ronde De nouveaux outils IA et data au service de
l’info

• Ioana Manolescu participated to the debate “IA, fake news, and young people’s distrust of the
media” on FranceInfo and Twitch, on Nov 22, 2023. Replay: Intelligence artificielle, défiance des
jeunes, fake news : les médias à l’heure des grandes crises

• Simon Ebel and Théo Galizzi participated to Capcom 2023, presenting StatCheck, developed in

collaboration with RadioFrance

• Oana Goga did an interview for Le Monde: Vidéos pour enfants : un ciblage publicitaire qui

contourne la réglementation

10.3.3 Interventions

•

• Nelly Barret: "User-oriented exploration of semi-structured datasets" @ LISN (Univ. Paris Saclay) in

Oct 2023

• Madhulika Mohanty: "Effective Exploration of Graph-structured Data" at BD, LIP6 in Apr 2023; BD,

LIRIS in Mar 2023 and LaHDaK, LISN in May 2023.

• Oana Balalau: "NLP for Journalism: Current Progress and Open Challenges", at Centrum Wiskunde

Informatica (CWI) in September 2023

• Oana Balalau: "CS for social good: 2018 vs 2023", at Max Planck Institute for Informatics, in

September 2023

11 Scientific production

11.1 Major publications

[1] R. Alotaibi, D. Bursztyn, A. Deutsch, I. Manolescu and S. Zampetakis. ‘Towards Scalable Hybrid
Stores: Constraint-Based Rewriting to the Rescue’. In: SIGMOD 2019 - ACM SIGMOD International
Conference on Management of Data. Amsterdam, Netherlands, June 2019. URL: https://hal.inr
ia.fr/hal-02070827.

[2] O. Balalau, S. Ebel, T. Galizzi, I. Manolescu, Q. Massonnat, A. Deiana, E. Gautreau, A. Krempf, T.
Pontillon, G. Roux and J. Yakin. ‘Fact-checking Multidimensional Statistic Claims in French’. In:
TTO 2022 - Truth and Trust Online. Boston [Hybrid Event], United States, 12th Oct. 2022. URL:
https://hal.science/hal-03791175.

[3] O. Balalau and R. Horincar. ‘From the Stage to the Audience: Propaganda on Reddit’. In: EACL
2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics.
Online, France, 19th Apr. 2021. URL: https://hal.inria.fr/hal-03351621.

[4] M. Buron, F. Goasdoué, I. Manolescu and M.-L. Mugnier. ‘Reformulation-based query answering for
RDF graphs with RDFS ontologies’. In: ESWC 2019 - European Semantic Web Conference. Portoroz,
Slovenia, Mar. 2019. URL: https://hal.archives-ouvertes.fr/hal-02051413.

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Inria Annual Report 2023

[5] D. Bursztyn, F. Goasdoué and I. Manolescu. ‘Teaching an RDBMS about ontological constraints’. In:
Very Large Data Bases. New Delhi, India, Sept. 2016. URL: https://hal.inria.fr/hal-0135459
2.

[6]

S. Cazalens, P. Lamarre, J. Leblay, I. Manolescu and X. Tannier. ‘A Content Management Perspective
on Fact-Checking’. In: The Web Conference 2018 - alternate paper tracks "Journalism, Misinforma-
tion and Fact Checking". Lyon, France, Apr. 2018, pp. 565–574. URL: https://hal.archives-ouv
ertes.fr/hal-01722666.

[7] S. Cebiric, F. Goasdoué, H. Kondylakis, D. Kotzinos, I. Manolescu, G. Troullinou and M. Zneika.
‘Summarizing Semantic Graphs: A Survey’. In: The VLDB Journal (2018). URL: https://hal.inri
a.fr/hal-01925496.

[8] Y. Diao, P. Guzewicz, I. Manolescu and M. Mazuran. ‘Spade: A Modular Framework for Analytical
Exploration of RDF Graphs’. In: VLDB 2019 - 45th International Conference on Very Large Data
Bases. Proceedings of the VLDB Endowment, Vol. 12, No. 12. Los Angeles, United States, Aug. 2019.
DOI: 10.14778/3352063.3352101. URL: https://hal.inria.fr/hal-02152844.

[9] E. Huang, L. Peng, L. D. Palma, A. Abdelkafi, A. Liu and Y. Diao. ‘Optimization for active learning-
based interactive database exploration’. In: Proceedings of the VLDB Endowment (PVLDB) 12.1
(Sept. 2018), pp. 71–84. DOI: 10.14778/3275536.3275542. URL: https://hal.inria.fr/hal-
01969886.

[10] A. Roy, Y. Diao, U. Evani, A. Abhyankar, C. Howarth, R. Le Priol and T. Bloom. ‘Massively Parallel
Processing of Whole Genome Sequence Data: An In-Depth Performance Study’. In: SIGMOD
’17 Proceedings of the 2017 ACM International Conference on Management of Dat. SIGMOD ’17
Proceedings of the 2017 ACM International Conference on Management of Data. SIGMOD ACM
Special Interest Group on Management of Data. Chicago, Illinois, United States: ACM, May 2017,
pp. 187–202. DOI: 10.1145/3035918.3064048. URL: https://hal.inria.fr/hal-01683398.

[11] S. Y. Sahai, O. Balalau and R. Horincar. ‘Breaking Down the Invisible Wall of Informal Fallacies in
Online Discussions’. In: ACL-IJCNLP 2021 - Joint Conference of the 59th Annual Meeting of the
Association for Computational Linguistics and the 11th International Joint Conference on Natural
Language Processing. Online, France, 2nd Aug. 2021. URL: https://hal.inria.fr/hal-033516
49.

11.2 Publications of the year

International journals

[12]

S. Amer-Yahia, D. Agrawal, Y. Amsterdamer, S. Bhowmick, A. Bonifati, R. Borovica-Gajic, J. Camacho-
Rodríguez, B. Catania, P. Chrysanthis, C. Curino, J. Darmont, G. Dobbie, A. El Abbadi, A. Floratou,
J. Freire, A. Jindal, V. Kalogeraki, S. Maiyya, A. Meliou, M. Mohanty, B. Omidvar-Tehrani, F. Özcan, L.
Peterfreund, W. Rahayu, S. Sadiq, S. Sellami, U. Sirin, W.-C. Tan, B. Thuraisingham, Y. Tian, P. Tözün,
G. Vargas-Solar, N. Yadwadkar, V. Zakhary and M. Zhang. ‘Diversity, Equity and Inclusion Activities
in Database Conferences: A 2022 Report’. In: SIGMOD record 52.2 (10th Aug. 2023), pp. 38–42. DOI:
10.1145/3615952.3615964. URL: https://hal.science/hal-04271933.

[13] L. Di Palma, D. Yanlei and L. Anna. ‘Efficient Version Space Algorithms for Human-in-the-loop
Model Development’. In: ACM Transactions on Knowledge Discovery from Data (TKDD) 18.3
(12th Jan. 2024), pp. 1–49. DOI: 10.1145/3637443. URL: https://inria.hal.science/ha
l-04414855.

[14] E. Huang, Y. Diao, A. Liu, L. Peng and L. D. Palma. ‘Efficient and robust active learning methods for
interactive database exploration’. In: The VLDB Journal (16th Nov. 2023). DOI: 10.1007/s00778-0
23-00816-x. URL: https://inria.hal.science/hal-04414815.

International peer-reviewed conferences

[15] A. C. Anadiotis, I. Manolescu and M. Mohanty. ‘Integrating Connection Search in Graph Queries’.
In: ICDE 2023 - 39th IEEE International Conference on Data Engineering. Anaheim (CA), United
States, 3rd Apr. 2023. URL: https://inria.hal.science/hal-04110779.

Project CEDAR

23

[16] A. C. Anadiotis, I. Manolescu and M. Mohanty. ‘More power to SPARQL: From paths to trees’. In:
Lecture Notes in Computer Science. ESWC 2023 - Extended Semantic Web Conference. Vol. LNCS-
13870. The Semantic Web - 20th International Conference, ESWC 2023. Hersonissou, Crete, Greece,
28th May 2023. URL: https://inria.hal.science/hal-04102807.

[17] N. Barret, S. Ebel, T. Galizzi, I. Manolescu and M. Mohanty. ‘User-friendly exploration of highly
heterogeneous data lakes’. In: CoopIS 2023 - International Conference on Cooperative Information
Systems. Groningen, Netherlands, 30th Oct. 2023. URL: https://hal.science/hal-04185938.

[18] N. Barret, A. Gauquier, J.-J. Law and I. Manolescu. ‘Exploring heterogeneous data graphs through
their entity paths’. In: ADBIS 2023 - 27th European Conference on Advances in Databases and
Information Systems. Barcelona, Spain, 4th Sept. 2023. URL: https://hal.science/hal-04131
977.

[19] N. Barret, A. Gauquier, J.-J. Law and I. Manolescu. ‘PathWays: entity-focused exploration of het-
erogeneous data graphs’. In: ESWC 2023 - 20th European Semantic Web Conference. Hersonissos
(Crete), Greece, 28th May 2023. URL: https://hal.science/hal-04103293.

[20] N. Barret, I. Manolescu and P. Upadhyay. ‘Computing Generic Abstractions from Application
Datasets’. In: OpenProceedings. EDBT 2024 - 27th International Conference on Extending Database
Technology. Vol. 27. Paestum, Italy, 25th Mar. 2024, pp. 94–107. URL: https://hal.science/hal-
04131974.

[21]

S. Chouaki, O. Goga, H. Haddadi and P. Snyder. ‘Understanding the Privacy Risks of Popular Search
Engine Advertising Systems’. In: ACM IMC 2023 - Internet Measurement Conference 2023. Montréal,
Canada, 24th Oct. 2023. DOI: 10.1145/3618257.3624823. URL: https://inria.hal.science
/hal-04228304.

[22] E. Gkiouzepi, A. Andreou, O. Goga and P. Loiseau. ‘Collaborative Ad Transparency: Promises and
Limitations’. In: SP 2023 - 44th IEEE Symposium on Security and Privacy. San Francisco, United
States, 22nd May 2023. URL: https://inria.hal.science/hal-03916393.

[23]

I. Manolescu and M. Mohanty. ‘Full-Power Graph Querying: State of the Art and Challenges’. In:
VLDB 2023 - 49th International Conference on Very Large Data Bases. Vancouver, Canada, 28th Aug.
2023. DOI: 10.14778/3611540.3611577. URL: https://inria.hal.science/hal-04199455.

[24] T. Medjkoune, O. Goga and J. Sénéchal. ‘Marketing to Children Through Online Targeted Advertising:
Targeting Mechanisms and Legal Aspects’. In: The 2023 ACM SIGSAC Conference on Computer
and Communications Security. Copenhague, Denmark: ACM, 1st Nov. 2023. DOI: 10.1145/35769
15.3623172. URL: https://hal.univ-lille.fr/hal-04311598.

[25] V. Sosnovik, R. Kessi, M. Coavoux and O. Goga. ‘On Detecting Policy-Related Political Ads: An Explor-
atory Analysis of Meta Ads in 2022 French Election’. In: WWW 2023 - The ACM Web Conference 2023.
Proceedings of the ACM Web Conference 2023. Austin Texas, United States: ACM, 30th Apr. 2023,
pp. 4104–4114. DOI: 10.1145/3543507.3583875. URL: https://hal.science/hal-04129915.

[26] P. Upadhyay, O. Balalau and I. Manolescu. ‘Open Information Extraction with Entity Focused
Constraints’. In: EACL 2023 - 17th Conference of the European Chapter of the Association for Com-
putational Linguistics. Dubrovnik, Croatia, 2nd May 2023. URL: https://inria.hal.science/h
al-03980046.

[27] K. Zhang, O. Balalau and I. Manolescu. ‘FactSpotter: Evaluating the Factual Faithfulness of Graph-
to-Text Generation’. In: Findings of EMNLP 2023 - Conference on Empirical Methods in Natural
Language Processing. Singapore, Singapore, 6th Dec. 2023. URL: https://hal.science/hal-04
257838.

National peer-reviewed Conferences

[28] A. Anadiotis, M. Ghufran Khan and I. Manolescu. ‘Scalable Analytics on Multi-Streams Dynamic
Graphs’. In: BDA 2023 - 39th Conference on Data Management – Principles, Technologies and
Applications. Montpellier, France, 5th July 2023. DOI: 10.1145/nnnnnnn.nnnnnnn. URL: https:
//hal.science/hal-04212814.

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Inria Annual Report 2023

Reports & preprints

[29] A. C. Anadiotis, I. Manolescu and M. Mohanty. Integrating Connection Search in Graph Queries.

Inria Saclay - Île de France, 4th Jan. 2023. URL: https://inria.hal.science/hal-03923293.

Other scientific publications

[30]

I. Manolescu. Understanding and Querying Data Regardless of the Data Model. Cluj-Napoca, Ro-
mania, 26th July 2023. URL: https://inria.hal.science/hal-04190926.
<scratchpad>
- Main topic: Research Centre CEDAR (Inria Saclay Centre at Institut Polytechnique of Paris)
- Key points:
  - Project CEDAR: Research focus on models, algorithms, and tools for efficient data and knowledge management
  - Four interconnected research areas:
    1. Optimization and performance at scale
    2. Data discovery and exploration
    3. Natural language understanding for analyzing and supporting digital arenas
    4. Safeguarding information systems
  - Application domains: Cloud computing, computational journalism, computational social science, online targeted advertising
  - Partnerships and cooperations: European initiatives (Horizon Europe, H2020), national initiatives (ANR), regional initiatives
- Structure: The document is organized into sections, including project team members, activity report, contents, application domains, social and environmental responsibility, and dissemination
- Potential artifacts to ignore: Web-related elements, such as links, HTML tags, and unnecessary web pages
</scratchpad>

<final_summary>
The document presents the research centre CEDAR, located at Inria Saclay Centre at Institut Polytechnique of Paris. The centre focuses on developing models, algorithms, and tools for efficient data and knowledge management, with a strong emphasis on performance at scale. The research is divided into four interconnected areas: optimization and performance at scale, data discovery and exploration, natural language understanding, and safeguarding information systems. The centre has partnerships and cooperations with various European, national, and regional initiatives. The research has applications in cloud computing, computational journalism, computational social science, and online targeted advertising. The centre aims to improve the quality of public debate with AI and mitigate risks of AI-driven information targeting.</final_summary>
The document presents the research centre CEDAR, located at Inria Saclay Centre at Institut Polytechnique of Paris. The centre focuses on developing models, algorithms, and tools for efficient data and knowledge management, with a strong emphasis on performance at scale. The research is divided into four interconnected areas: optimization and performance at scale, data discovery and exploration, natural language understanding, and safeguarding information systems. The centre has partnerships and cooperations with various European, national, and regional initiatives. The research has applications in cloud computing, computational journalism, computational social science, and online targeted advertising. The centre aims to improve the quality of public debate with AI and mitigate risks of AI-driven information targeting.