RESEARCH CENTRE

Inria Centre
at Université Grenoble Alpes

2023
ACTIVITY REPORT

IN PARTNERSHIP WITH:

Université de Grenoble Alpes

Project-Team

MOEX

Evolving Knowledge

IN COLLABORATION WITH: Laboratoire d’Informatique de Grenoble (LIG)

DOMAIN

Perception, Cognition and Interaction

THEME

Data and Knowledge Representation and
Processing

Contents

Project-Team MOEX

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

3.1 Knowledge and belief representation semantics . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Data interlinking with link keys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Experimental cultural knowledge evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Application domains

5 Highlights of the year

6 New software, platforms, open data

6.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.1 Lazylav . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.2 Alignment API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.3 LinkEx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Open data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7 New results

7.1 Cultural knowledge evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.1 Measuring knowledge diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.2 Pluripotent agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.3 Value-sensitive cultural evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.4 Intrinsic motivation, curiosity and creativity . . . . . . . . . . . . . . . . . . . . . . . .
7.1.5 Awareness and forgetting in dynamic epistemic logic . . . . . . . . . . . . . . . . . . .
7.1.6 A social science and the humanities approach to cultural knowledge evolution . . .
7.2 Link keys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2.1 Link key redundancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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8 Partnerships and cooperations

8.1 International research visitors

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.1.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.1.2 Visits to international teams

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8.3 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
8.3.1 ELKER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.3.2 MIAI

8.2 European initiatives

8.2.1 H2020 projects

9 Dissemination

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9.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
9.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
9.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
9.1.3 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
9.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
9.1.5 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 12
9.1.6 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
9.1.7 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
9.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
9.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
9.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

9.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
9.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
9.3.1 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

10 Scientific production

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10.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
10.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
10.3 Cited publications

Project MOEX

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Project-Team MOEX

Creation of the Project-Team: 2017 November 01

Keywords

Computer sciences and digital sciences

A3.2. – Knowledge

A3.2.1. – Knowledge bases

A3.2.2. – Knowledge extraction, cleaning

A3.2.4. – Semantic Web

A3.2.5. – Ontologies

A3.2.6. – Linked data

A6.1.3. – Discrete Modeling (multi-agent, people centered)

A7.2. – Logic in Computer Science

A9. – Artificial intelligence

A9.1. – Knowledge

A9.9. – Distributed AI, Multi-agent

Other research topics and application domains

B8.5. – Smart society

B9. – Society and Knowledge

B9.5.1. – Computer science

B9.7.2. – Open data

B9.8. – Reproducibility

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1 Team members, visitors, external collaborators

Research Scientist

• Jerome Euzenat [Team leader, INRIA, Senior Researcher, HDR]

Faculty Members

• Manuel Atencia [UGA, Associate Professor]

• Jérôme David [UGA, Associate Professor]

Post-Doctoral Fellow

• Helga Lendrin [INRIA, Post-Doctoral Fellow]

PhD Students

• Yasser Bourahla [UGA, until Aug 2023]

• Alda Canito [University of Porto, from Apr 2023 until Jul 2023]

• Andrea Kalaitzakis [UGA]

Interns and Apprentices

• Adrien Bonnardel [UGA, Intern, from Sep 2023]

• Charline Fiorentino [UGA, Intern, until Jun 2023]

• Adriana Luntraru [MIAI GRENOBLE ALPES, Intern, from Feb 2023 until Jul 2023]

• Giorgio Orioles [INRIA, Intern, from Feb 2023 until Jun 2023]

• Emma Rechon-Reguet [UGA, Intern, until May 2023]

• Anais Siebers [INRIA, Intern, from Apr 2023 until Sep 2023]

Administrative Assistant

• Julia Di Toro [INRIA]

Visiting Scientist

• Koji Hasebe [University of Tsukuba, from Jul 2023 until Jul 2023, Invited professor]

2 Overall objectives

Human beings are apparently able to communicate knowledge. However, it is impossible for us to know
if we share the same representation of knowledge.

mOeX addresses the evolution of knowledge representations in individuals and populations. The

ambition of the mOeX project is to answer, in particular, the following questions:

• How do agent populations adapt their knowledge representation to their environment and to other

populations?

• How must this knowledge evolve when the environment changes and new populations are en-

countered?

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• How can agents preserve knowledge diversity and is this diversity beneficial?

We study them chiefly in a well-controlled computer science context.

For that purpose, we combine knowledge representation and cultural evolution methods. The former
provides formal models of knowledge; the latter provides a well-defined framework for studying situated
evolution.

We consider knowledge as a culture and study the global properties of local adaptation operators

applied by populations of agents by jointly:

• experimentally testing the properties of adaptation operators in various situations using experi-

mental cultural evolution, and

• theoretically determining such properties by modelling how operators shape knowledge represent-

ation.

We aim at acquiring a precise understanding of knowledge evolution through the consideration of a

wide range of situations, representations and adaptation operators.

In addition, we still investigate RDF data interlinking with link keys, a way to link entities from different

data sets.

3 Research program

3.1 Knowledge and belief representation semantics

We work with knowledge and beliefs represented in computers. In principle, the difference between them
is based on their epistemological status: knowledge is true belief. But they can both be expressed in the
same knowledge representation languages (like description logics, conceptual graphs and object-based
languages). Their semantics is usually defined within model theory initially developed for logics.

We consider a language L as a set of syntactically defined expressions (often inductively defined by
applying constructors over other expressions). A representation (o ⊆ L) is a set of such expressions. It
may also be called an ontology. An interpretation function (I ) is inductively defined over the structure of
the language to a structure called the domain of interpretation (D). This expresses the construction of
the “meaning” of an expression in function of its components. A formula is satisfied by an interpretation
if it fulfills a condition (in general being interpreted over a particular subset of the domain). A model of a
set of expressions is an interpretation satisfying all the expressions. A set of expressions is said consistent
if it has at least one model, inconsistent otherwise. An expression (δ) is then a consequence of a set of
expressions (o) if it is satisfied by all of their models (noted o |= δ).

The languages dedicated to the semantic web (RDF and OWL) follow that approach. RDF is a know-
ledge representation language dedicated to the description of resources; OWL is designed for expressing
ontologies: it describes concepts and relations that can be used within RDF.

A computer must determine if a particular expression (taken as a query, for instance) is the con-
sequence of a set of axioms (a knowledge base). For that purpose, it uses programs, called provers,
that can be based on the processing of a set of inference rules, on the construction of models or on
procedural programming. These programs are able to deduce theorems (noted o ⊢ δ). They are said to be
sound if they only find theorems which are indeed consequences and to be complete if they find all the
consequences as theorems.

3.2 Data interlinking with link keys

Vast amounts of RDF data are made available on the web by various institutions providing overlapping
information. To be fully exploited, different representations of the same object across various data sets,
often using different ontologies, have to be identified. When different vocabularies are used for describing
data, it is necessary to identify the concepts they define. This task is called ontology matching and its
result is an alignment A, i.e. a set of correspondences 〈e, r, e
of two different
ontologies by a particular relation r (which may be equivalence, subsumption, disjointness, etc.) [4].

′〉 relating entities e and e

′

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Inria Annual Report 2023

At the data level, data interlinking is the process of generating links identifying the same resource
) it generates a link

described in two data sets. Parallel to ontology matching, from two datasets (d and d
set made of pairs of resource identifier.

′

We have introduced link keys [4, 1, 11] which extend database keys in a way which is more adapted to

RDF and deals with two data sets instead of a single relation. An example of a link key expression is:

{〈auteur,creator〉}{〈titre,title〉} l i nkke y 〈Livre,Book〉

stating that whenever an instance of the class Livre has the same values for the property auteur as an
instance of class Book has for the property creator and they share at least one value for their property
titre and title, then they denote the same entity. More precisely, a link key is a structure 〈K eq , K i n,C 〉 such
that:

• K eq and K i n are sets of pairs of property expressions;

• C is a pair of class expressions (or a correspondence).

Such a link key holds if and only if for any pair of resources belonging to the classes in correspondence
such that the values of their property in K eq are pairwise equal and the values of those in K i n pairwise
intersect, the resources are the same. Link keys can then be used for finding equal individuals across
two data sets and generating the corresponding owl:sameAs links. Link keys take into account the non
functionality of RDF data and have to deal with non literal values. In particular, they may use arbitrary
properties and class expressions. This renders their discovery and use difficult.

3.3 Experimental cultural knowledge evolution

Cultural evolution applies a generalised version of the theory of evolution to culture. It considers how
culture spreads and evolves within human societies [17]. In computer science, cultural evolution experi-
ments are performed through multi-agent simulation: a society of agents adapts its culture through a
precisely defined protocol: agents perform repeatedly and randomly a specific task, called game, and
their evolution is monitored. This aims at discovering experimentally the states that agents reach and the
properties of these states.

Experimental cultural evolution has been successfully and convincingly applied to the evolution
of natural languages [18]. Agents play language games and adjust their vocabulary and grammar as
soon as they are not able to communicate properly, i.e. they misuse a term or they do not behave in
the expected way. It showed its capacity to model various such games in a systematic framework and to
provide convincing explanations of linguistic phenomena.

We adapt this experimental strategy to knowledge representation [2]. Agents use their, shared or
private, knowledge to play games and, in case of failure, they use adaptation operators to modify this
knowledge. We monitor the evolution of agent knowledge with respect to their ability to perform the
game (success rate) and with respect to the properties satisfied by the resulting knowledge itself. Such
properties may, for instance, be:

• Agents converge to a common knowledge representation (a convergence property).

• Agents converge towards different but compatible (logically consistent) knowledge (a logical epi-

stemic property), or towards closer knowledge (a metric epistemic property).

• That under the threat of a changing environment, agents that have operators that preserve diverse
knowledge recover faster from the changes than those that have operators that converge towards a
single representation (a differential property under environment change).

Our goal is to determine which operators are suitable for achieving desired properties in the context

of a particular game.

Project MOEX

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4 Application domains

Our work on data interlinking aims at application to linked data offered in RDF on the web. It has found
applications in thesaurus and bibliographical data interlinking.

mOeX’s work on cultural knowledge evolution is not directly applied and rather aims at isolating
general principles of knowledge evolution. However, we foresee its potential impact in the long term in
fields such as smart cities, the internet of things or social robotics in which the knowledge acquired by
autonomous agents will have to be shared and adapted to changing situations.

5 Highlights of the year

The highlights of the year are undoubtly Yasser Bourahla’s Phd thesis “Multi-agent simulation of cultural
ontology evolution through interaction” [10], and Jérôme David’s habilitation “Measures for knowledge –
with applications to ontology matching and data interlinking” [11].

6 New software, platforms, open data

6.1 New software

6.1.1 Lazylav

Name: Lazy lavender

Keywords: Reproducibility, Multi-agent, Simulation

Scientific Description: Lazy lavender aims at supporting mOeX’s research on simulating knowledge evol-
ution. It is not a general purpose simulator. However, it features some methodological innovations
in term of facilitating publication, recording, and replaying of experiments.

Functional Description: Lazy Lavender is a simulation environment for cultural knowledge evolution,
i.e. running randomised experiments with agent adjusting their knowledge while attempting to
communicate. It can generate detailed report and data from the experiments and directions to
repeat them.

Release Contributions: Lazy is continuously evolving and do not feature stable releases.

Instead, use git hashes to determine which version is used in a simulation.

URL: https://gitlab.inria.fr/moex/lazylav/

Publications: hal-03426130, hal-01661140, hal-01661139, hal-01180916, hal-03939919, hal-03905183

Contact: Jerome Euzenat

Participants: Jerome Euzenat, Yasser Bourahla, Iris Lohja, Fatme Danash, Irina Dragoste, Andrea Kalaitza-

kis

6.1.2 Alignment API

Keywords: Ontologies, Alignment, Ontology engineering, Knowledge representation

Scientific Description: The API itself is a Java description of tools for accessing the common format. It

defines five main interfaces (OntologyNetwork, Alignment, Cell, Relation and Evaluator).

We provide an implementation for this API which can be used for producing transformations, rules
or bridge axioms independently from the algorithm that produced the alignment. The proposed
implementation features: - a base implementation of the interfaces with all useful facilities, - a
library of sample matchers, - a library of renderers (XSLT, RDF, SKOS, SWRL, OWL, C-OWL, SPARQL),
- a library of evaluators (various generalisation of precision/recall, precision/recall graphs), - a

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Inria Annual Report 2023

flexible test generation framework that allows for generating evaluation data sets, - a library of
wrappers for several ontology APIs , - a parser for the format.

The API implementation provides an extended language for expressive alignments (EDOAL). EDOAL
supports many types of restrictions inspired from description logics as well as link keys. It is fully
supported for parsing and serialising in XML. It also provide other serialisers, to OWL and SPARQL
queries in particular.

To instanciate the API , it is sufficient to refine the base implementation by implementing the align()
method. Doing so, the new implementation will benefit from all the services already implemented
in the base implementation.

Functional Description: Using ontologies is the privileged way to achieve interoperability among het-
erogeneous systems within the Semantic web. However, as the ontologies underlying two systems
are not necessarily compatible, they may in turn need to be reconciled. Ontology reconciliation
requires most of the time to find the correspondences between entities (e.g. classes, objects,
properties) occurring in the ontologies. We call a set of such correspondences an alignment.

Release Contributions: See release notes.

This is the last release made from gforge svn repository. After it, the Alignment API is hosted by
gitlab and versioned with git. It may well be the last formal release, clone from the repo instead.

The Alignment API compiles in Java 11 (jars are still compiled in Java 8).

URL: https://moex.gitlabpages.inria.fr/alignapi/

Publications: hal-00825931, hal-00781018

Contact: Jerome Euzenat

Participants: Armen Inants, Chan Le Duc, Jérôme David, Jerome Euzenat, Jérôme Pierson, Luz Maria

Priego-Roche, Nicolas Guillouet

6.1.3 LinkEx

Keywords: LOD - Linked open data, Data interlinking, Formal concept analysis

Functional Description: LinkEx implements link key candidate extraction with our initial algorithms,
formal concept analysis or pattern structures. It can extract link key expressions with inverse
and composed properties and generate compound link keys. Extracted link key expressions may
be evaluated using various measures, including our discriminability and coverage. It can also
evaluate them according to an input link sample. The set of candidates can be rendered within the
Alignment API’s EDOAL language or in dot.

URL: https://gitlab.inria.fr/moex/linkex

Publications: hal-02168775, hal-01179166

Author: Jérôme David

Contact: Jérôme David

6.2 Open data

The experimental activity of the mOeX team is logged into the Sakere platform (https://sake.re).
It serves as a repository of experiment logbooks providing access to simulation software, experiment
scripts and parameters, resulting raw data and analysis notebooks. The experimentation and publication
process is documented on the site. This data is fully retrievable and auditable through git.

Project MOEX

7 New results

7.1 Cultural knowledge evolution

7

In 2023, the work on cultural knowledge evolution has progressed in several directions, tasks, values,
motivations, all based on the protocol described in the PhD dissertation of Yasser Bourahla [10].

Concerning link keys the work on extracting link keys led to the PhD dissertation of Nacira Abbas and

part of the habilitation of her supervisor Jérôme David [11].

7.1.1 Measuring knowledge diversity

Participants:

Adrien Bonnardel, Yasser Bourahla, Jérôme David, Jérôme Eu-
zenat (Correspondent).

Assessing knowledge diversity is useful in our experiments. We had proposed to use an entropy-based
diversity measure inspired from [16] and using an ontology distance measure. However, in such measures
the maximal diversity is not obtained with a uniform distribution and it has to be computed. This year,
we implemented its computation and developed a normalised version of this measure. It should help
reconsidering the results previously obtained on knowledge diversity [10] with this new method.

7.1.2 Pluripotent agents

Participants:

Jérôme Euzenat, Andrea Kalaitzakis (Correspondent).

Following last years’ results, we continued investigating resource-limited agents tackling multiple
tasks. We found, by varying the number of tasks assigned to each agent and the number of common
properties across these tasks, that agents transfer knowledge from one task to another [7]. In addition, we
showed that agents with limited memory will specialize on a subset of tasks, whose number depends
on available memory. However, it seems that maximising task accuracy and achieving consensus are
mutually exclusive [8].

When imposing an equidistribution of the tasks that agents play, global benefits have been identified.
First, the agents achieve comparable precision in all tasks. Second, the total wealth acquired by agents
is higher and more equitably distributed, but lower than with unlimited agents. The wealth is the
accumulation of points acquired when agents make the correct decision. The question is then how could
agents adopt this behaviour without imposing it. We attempted to reach this goal by replacing agents
from one generation to the other favouring those disagreeing more often (lower success rate), assuming
that they have rare knowledge. However, this only has a temporary effect.

This work is part of the PhD thesis of Andreas Kalaitzakis.

7.1.3 Value-sensitive cultural evolution

Participants:

Jérôme Euzenat (Correspondent), Adriana Luntraru.

Cultural values are cognitive representations of general objectives, such as independence or mastery,
that people use to distinguish whether something is “good” or “bad”. More specifically, people may use
their values to evaluate alternatives and pick the most compatible one. We consider values as grounding
agent behaviour in cultural knowledge evolution, and more specifically in the way they evolve their
ontologies. We used the cultural values of independence, novelty, authority and mastery to influence
the choice of which agent adapts in a population of agents sharing the same values. From a weighted
aggregation of how much agents adhere to these values, they determine which one adapts its knowledge
when two agents disagree, e.g. novelty will give more change to rare knowledge, though mastery will

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Inria Annual Report 2023

promote knowledge that has proved efficient. Results [14] show that agents do not improve the accuracy
of their knowledge without using the mastery value (akin the success biais in social learning [10]). Under
certain conditions, independence causes the agents to converge to successful interactions faster, and
novelty increases knowledge diversity, but both effects come with a large reduction in accuracy. We did
not find any significant effects of authority.

This was the master work of Adriana Luntraru.

7.1.4 Intrinsic motivation, curiosity and creativity

Participants:

Jérôme Euzenat (Correspondent), Anaïs Siebers.

In cultural knowledge evolution simulations, agent knowledge might be confined to specific areas
because the tasks that they perform do not require them to explore more. We considered how agents may
be provided with the intrinsic motivation to explore and how this affects their knowledge. Three different
kinds of motivation were investigated: curiosity (the will to explore the unknown), creativity (the will to
act differently) and non-exploration (the will to not explore new things). Moreover, intrinsic motivation
was modelled directly or learned through reinforcement learning. Finally, agents either explored on their
own or picked specific interaction partner(s). We have shown that such settings may have a significant
effect on the agent knowledge [15]. Contrary to the expectations and other studies, this did not lead
to an increase in knowledge completeness. Out of all intrinsic motivations, curiosity had the highest
accuracy and completeness. Models with reinforcement learning performed similarly as direct models.
As expected, intrinsic motivation led to faster convergence of the agents’ knowledge, especially with
social agents. Heterogeneously motivated agents only had a higher accuracy and completeness than
homogeneously motivated agents in specific cases.
This was the master work of Anaïs Siebers.

7.1.5 Awareness and forgetting in dynamic epistemic logic

Participants: Manuel Atencia, Jérôme Euzenat (Correspondent), Line van den Berg.

Agents use their own vocabularies to reason and talk about the world. Public signature awareness is
satisfied if agents are aware of the vocabularies, or signatures, used by all agents. Logics and in particular
Dynamic Epistemic Logic rely on public signature awareness. However, this assumption is not desirable
for dynamic and open multi-agent systems because it prevents agents to use vocabulary other agents
are unaware of and to extend it when encountering new information. We propose a new semantics for
awareness that enables us to drop public signature awareness [6]. This semantics is based on partial
valuation functions and weakly reflexive relations. We added dynamic modalities for raising public and
private awareness in which a distinction is made between becoming aware of a proposition and learning
its truth value. As a result, the semantics introduced can be used to model agent interaction without the
public signature awareness assumption.

This work is part of the PhD thesis of Line van den Berg defended in October 2021.

7.1.6 A social science and the humanities approach to cultural knowledge evolution

Participants: Helga Lendrin (Correspondent), Jérôme Euzenat.

We defined a protocol to experiment with human learners various modalities of knowledge transmis-
sion based on the Class? game that we designed. In particular, we want to test the effect of the medium
on knowledge acquisition and evolution. This protocol involves groups of pupils learning how to play the

Project MOEX

9

game and transmitting it to the next group. Transmission modalities varied in terms of face-to-face phys-
ical presence, online or written transmission. A series of tests have been designed in order to determine
which playing strategies are acquired by pupils after transmission and after playing.

7.2 Link keys

The investigation of link keys has been pursued following the properties of link key extraction (§3.2).

7.2.1 Link key redundancy

Participants:

Jérôme David (Correspondent).

We have continued studying how Formal Concept Analysis and its extensions are well adapted to
support the discovery of link keys. Different link key candidates can generate sets of identity links between
individuals that can be considered as equal when they are regarded as partitions of the identity relation
and thus involving a kind of redundancy. We have studied such a redundancy through partition pattern
structures [5]. In particular, experiments showed that redundancy of link key candidates while not
significant when based on identity of partitions appears to be much more significant when based on
similarity.

This work is part of the PhD thesis of Nacira Abbas, co-supervised by Jérôme David and Amedeo

Napoli (LORIA).

8 Partnerships and cooperations

8.1 International research visitors

8.1.1 Visits of international scientists

Participants: Koji Hasebe.

Country: Japan

Dates: 2023-07-01 – 2023-07-31

Context of the visit: This visit was held on the context of the collaboration with the multi-agent system

lab at the University of Tsukuba.

Mobility program/type of mobility: This visit was supported by an invited professor position from IN-

RIA.

8.1.2 Visits to international teams
Research stays abroad

Participants:

Jérôme Euzenat.

Visited institution: University of Tsukuba

Country: Japan

Dates: 2023-10-25 – 2023-1-13

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Inria Annual Report 2023

Context of the visit: This visit was held on the context of the collaboration with the multi-agent system

lab at the University of Tsukuba.

Mobility program/type of mobility: This has been a research stay supported by the Kyoukan PAI Project

(20 006913 01 9183) supported by the Région Auvergne Rhône-Alpes.

8.2 European initiatives

8.2.1 H2020 projects

Participants:

Jérôme Euzenat (Correspondent), Yasser Bourahla, Andreas Kalaitzakis.

Since September 2023, Jérôme Euzenat represents the University Grenoble Alpes within the TAILOR

network:

Program: H2020-ICT-48-2020

Title: Trustworthy AI through the integration of learning, optimisation and reasoning

Partner Institution(s):

• Linköping University (coordinator)

• CNR, INRIA, University college Cork, KU Leuven. . . Université Grenoble Alpes. . .

Duration: September 2020–August 2024

Web site: https://tailor-network.eu/

Abstract: UGA is mostly involved in the work packages concerning trustworthy AI and social AI.

8.3 National initiatives

8.3.1 ELKER

Participants: Manuel Atencia (Correspondent), Jérôme David, Jérôme Euzenat.

mOeX coordinates the ELKER project:

Program: ANR-PRC

Title: Extending link keys: extraction and reasoning

Partner Institution(s):

• Université Grenoble Alpes (coordinator)

• INRIA Nancy Lorraine (Orpailleur)

• Université Paris 13

Duration: October 2017–March 2023

Web site: https://project.inria.fr/elker/

Abstract: The goal of ELKER is to extend the foundations and algorithms of link keys (see §3.2) in two
complementary ways: extracting link keys automatically from datasets and reasoning with link
keys.

Project MOEX

8.3.2 MIAI

11

Participants: Manuel Atencia, Jérôme David, Jérôme Euzenat (Correspondent),

Yasser Bourahla, Andreas Kalaitzakis.

mOeX holds the MIAI Knowledge communication and evolution chair

Program: ANR-3IA

Title: Multidisciplinary institute in artificial intelligence

Partner Institution(s):

• Université Grenoble Alpes (coordinator)

Duration: July 2019–December 2023

Web site: https://miai.univ-grenoble-alpes.fr

Abstract: The MIAI Knowledge communication and evolution chair aims at understanding and de-
veloping mechanisms for seamlessly improving knowledge (see §3.3). It studies the evolution
of knowledge in a society of people and AI systems by applying evolution theory to knowledge
representation.

9 Dissemination

Participants:

Jérôme David, Jérôme Euzenat, Helga Lendrin.

9.1 Promoting scientific activities

9.1.1 Scientific events: organisation

Member of the organizing committees

• Jérôme Euzenat had been organiser of the 18th Ontology matching workshop of the 23th ISWC,
held online, 2023 (with Pavel Shvaiko, Ernesto Jiménez Ruiz, Cássia Trojahn dos Santos and Oktie
Hassanzadeh) [9]

9.1.2 Scientific events: selection

Member of the conference program committees

• Jérôme Euzenat had been programme committee member of the “International Joint Conference

on Artificial Intelligence (IJCAI)”

• Jérôme Euzenat had been programme committee member of the “European Conference on Artifi-

cial Intelligence (ECAI)”.

• Jérôme David and Jérôme Euzenat had been programme committee member of the “International

semantic web conference (ISWC)”.

• Jérôme Euzenat had been programme committee member of the “International conference on

formal ontologies for information systems (FOIS)”.

• Jérôme Euzenat had been programme committee member of the “International conference on

conceptual structures (ICCS)”.

• Jérôme David had been programme committee member of the “European semantic web conference

(ESWC)”.

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Inria Annual Report 2023

• Jérôme David had been programme committee member of “Extraction et gestion des connaissances

(EGC)”.

• Jérôme David had been programme committee member of “Ingénierie des connaissances (IC)”.

• Jérôme Euzenat had been programme committee member of the “Journées Françaises d’intelligence

artificielle fondamentale (JIAF)”.

9.1.3 Journal

Member of the editorial boards

• Jérôme Euzenat is member of the editorial board of Journal of web semantics (area editor) and the

Semantic web journal.

9.1.4 Invited talks

• Jérôme Euzenat had been invited to the SESAME seminar to give a talk on “Multi-agent simulation

of cultural knowledge evolution”, Montpellier (FR), 2023-02-17

• Jérôme Euzenat had been invited to the Dagsthul seminar on “Autonomous agents on the web”,

Wadern (DE), 2023-02-19 – 24 [13]

9.1.5 Leadership within the scientific community

• Jérôme David is member of the board of the Extraction and gestion des connaissances (Knowledge

extraction and management) conference series.

• Jérôme Euzenat is EurAI fellow.

9.1.6 Scientific expertise

• Jérôme Euzenat had been member of the Interdisciplinary assessment board for the interdisciplin-
ary and cross-disciplinary research programme in Artificial Intelligence of the Spanish Ministry of
Universities, 2023.

• Jérôme David had been president of the selection committee for an assistant professor (section 27)

at Université Grenoble Alpes (SHS department)

• Jérôme Euzenat member of the selection committee for the associate professor position 27MCF1141

at Université de Lorraine (IUT Saint-Dié)

• Jérôme David had been member of the selection committee for lecturer position (Polytech’Nantes)

• Jérôme Euzenat was evaluator for exchange grants of the European project TAILOR (member of the

“connectivity fund scientific board”)

9.1.7 Research administration

• Jérôme David is member of the LIG laboratory council.

• Jérôme David is member of the local “Commission du développement technologique”.

• Jérôme Euzenat is member of the local COS (Scientific Orientation Committee).

9.2 Teaching - Supervision - Juries

9.2.1 Teaching

Responsibilities

• Jérôme David is coordinator of the Master “Mathematiques et informatiques appliquées aux

sciences humaines et sociales” (Univ. Grenoble Alpes);

Project MOEX

Lectures

13

• Licence: Jérôme David, Algorithmique et programmation par objets, 70h/y, L2 MIASHS, UGA,

France

• Licence: Jérôme David, Système, L3 MIASHS, 18h/y, UGA, France

• Licence: Jérôme David, Programmation par objets avancée et structures de données, 18h/y, L3

MIASHS, UGA, France

• Licence: Jérôme David, Reseaux, 12h/y, L3 MIASHS, UGA, France

• Licence: Yasser Bourahla, Langage formel, 38h/y, L2 MIASHS, UGA, France

• Licence: Yasser Bourahla, Programmation fonctionnelle, 41h/y, L1 MIASHS, UGA, France

• Licence: Yasser Bourahla, Programmation Logique, 22h/y, L2 MIASHS, UGA, France

• Licence: Yasser Bourahla, Projet de programmation, 20h/y, M1 MIASHS, UGA, France

• Licence: Yasser Bourahla, Système d’exploitation, 12h/y, L3 MIASHS, UGA, France

• Licence: Yasser Bourahla, Soutient algorithmique, 10h/y, L3 MIASHS, UGA, France

• Licence: Yasser Bourahla and Jérôme Euzenat, Programmation Logique, L2 MIASHS, 34h/y, UGA,

France

• Master: Jérôme David, Systèmes d’exploitation, M1 DCISS, 18h/y, UGA, France

• Master: Jérôme David, Programmation Java 2, 30h/y, M1 MIASHS, UGA, France

• Master: Jérôme David, Stage de programmation, 10h/y, M2 MIASHS, UGA, France

• Master: Jérôme David, Soutien programmation, 10h/y, M1 MIASHS, UGA, France

• Master: Jérôme Euzenat, Semantics of distributed knowledge, 27h/y, M2R MoSIG, UGA, France

9.2.2 Supervision

• Nacira Abbas, “Analyse formelle de concepts pour la découverte de clés de liage dans le web des
données”, https://www.theses.fr/2023UPASG045, defended on 2023-10-13 (Jérôme David and
Amedeo Napoli)

• Yasser Bourahla, “Multi-agent simulation of cultural ontology evolution through interaction”, [10],

defended on 2023-07-11, (Manuel Atencia and Jérôme Euzenat)

• Andreas Kalaitzakis, “Effects of collaboration and specialisation on agent knowledge evolution”, in

progress since 2020-10-01 (Jérôme Euzenat)

9.2.3 Juries

• Jérôme David was reviewer and member of the PhD committee of Armita Khajeh Nassiri on
“Expressive Rule Discovery for Knowledge Graph Refinement”, Université Paris-Saclay, 2023-07-13

• Jérôme Euzenat was member of the PhD committee of Fatima Danash on “FORT: a modular
Foundational Ontological Relations Theory for representing and reasoning over the composition of
tangible entities – observations from cultural heritage”, Université Grenoble-Alpes, 2023-09-28

• Jérôme Euzenat was reviewer and member of the PhD committee of Abdelhafid Dahhani on
“Exploitation de l’isomorphisme de graphe et de l’alignement d’ontologies pour enrichir les objets
logiciels de connaissances sémantiques – application aux objets sages”, Université de Savoie Mont-
Blanc, 2023-12-14

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Inria Annual Report 2023

9.3 Popularization

9.3.1 Interventions

• Presentation of the Class? game to the French «info sans ordi (computer science unplugged)» group,

Lyon (FR), 2023-10-04

• Introduction of the Class? game to a tenth graders (2nd MathC2+), Montbonnot (FR), 2023-06-28.

• Introduction of the Class? game to tenth grader class within the Fête de la science (Science fair),

Montbonnot (FR), 2023-10-09

10 Scientific production

10.1 Major publications

[1] M. Atencia, J. David and J. Euzenat. ‘On the relation between keys and link keys for data interlinking’.
In: Semantic Web – Interoperability, Usability, Applicability 12.4 (2021), pp. 547–567. DOI: 10.3233
/SW-200414. URL: https://hal.science/hal-03426150.

[2] Y. Bourahla, M. Atencia and J. Euzenat. ‘Knowledge improvement and diversity under interaction-
driven adaptation of learned ontologies’. In: AAMAS 2021 - 20th ACM international conference on
Autonomous Agents and Multi-Agent Systems. London, United Kingdom, 2021, pp. 242–250. URL:
https://hal.archives-ouvertes.fr/hal-03426130.

[3]

[4]

J. Euzenat. ‘Interaction-based ontology alignment repair with expansion and relaxation’. In: Proc.
26th International Joint Conference on Artificial Intelligence (IJCAI), Melbourne (VIC AU). 2017,
pp. 185–191.
J. Euzenat and P. Shvaiko. Ontology matching. en. 2nd . Heidelberg (DE): Springer-Verlag, 2013.
520 pp. URL: http://book.ontologymatching.org.

10.2 Publications of the year

International journals

[5] N. Abbas, A. Bazin, J. David and A. Napoli. ‘Discovery of link keys in resource description framework
datasets based on pattern structures’. In: International Journal of Approximate Reasoning 161 (Oct.
2023), p. 108978. DOI: 10.1016/j.ijar.2023.108978. URL: https://hal.science/hal-0436
8386.

[6] L. van den Berg, M. Atencia and J. Euzenat. ‘Raising awareness without disclosing truth’. In: Annals
of Mathematics and Artificial Intelligence 91.4 (2023), pp. 431–464. DOI: 10.1007/s10472-022-09
809-y. URL: https://hal.science/hal-04351134.

International peer-reviewed conferences

[7] A. Kalaitzakis and J. Euzenat. ‘Multi-tasking resource-constrained agents reach higher accuracy
when tasks overlap’. In: Proc. 20th European conference on multi-agents systems (EUMAS). EUMAS
2023 - 20th European conference on multi-agents systems. Napoli, Italy: Springer Verlag, 2023,
pp. 425–434. DOI: 10.1007/978-3-031-43264-4_28. URL: https://hal.science/hal-04351
111.

National peer-reviewed Conferences

[8] A. Kalaitzakis and J. Euzenat. ‘À quoi sert la spécialisation en évolution culturelle de la connais-
sance?’ In: Actes 31e journées francophones sur Systèmes multi-agent (JFSMA). JFSMA 2023 - 31e
journées francophones sur Systèmes multi-agent. Strasbourg, France: No commercial editor., 2023,
pp. 76–85. URL: https://hal.science/hal-04351105.

Project MOEX

15

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

[9] P. Shvaiko, J. Euzenat, E. Jiménez-Ruiz, O. Hassanzadeh and C. Trojahn, eds. Proceedings of The 18th
International Workshop on Ontology Matching (OM 2023) collocated with the 22nd International
Semantic Web Conference ISWC-2023 November 7th, 2023, Athens, Greece. 18th ISWC workshop on
ontology matching (OM 2023). Vol. 3591. ceur-ws.org, 2023, pp. 1–202. URL: https://hal.scienc
e/hal-04351128.

Doctoral dissertations and habilitation theses

[10] Y. Bourahla. ‘Multi-agent simulation of cultural ontology evolution through interaction’. Université
Grenoble Alpes [2020-....], 11th July 2023. URL: https://theses.hal.science/tel-04347698.

[11]

J. David. ‘Measures for knowledge – with applications to ontology matching and data interlinking’.
Université Grenoble Alpes, 15th May 2023. URL: https://theses.hal.science/tel-04357558.

Reports & preprints

[12]

J. Euzenat. Stepwise functional refoundation of relational concept analysis. 9518. INRIA, Oct. 2023,
p. 73. URL: https://hal.science/hal-04230481.

Other scientific publications

[13]

J. Euzenat. ‘Society = Autonomy + Adaptation’. In: Proc. Dagstuhl seminar on Agents on the web.
Dagstuhl seminar on Agents on the web 2023. Wadern, Germany: Schloß Dagstuhl - Leibniz-
Zentrum für Informatik, 2023, p. 86. URL: https://hal.science/hal-04351093.

[14] A. Luntraru. ‘Value-sensitive knowledge evolution’. Master’s thesis, Université Grenoble Alpes,

Grenoble (FR), 27th June 2023. URL: https://inria.hal.science/hal-04351158.

[15] A. Siebers. ‘Intrinsic exploration-motivation in cultural knowledge evolution’. Master’s thesis, Ruhr
Universität, Bochum (DE), 25th Sept. 2023. URL: https://inria.hal.science/hal-04351162.

10.3 Cited publications

[16] T. Leinster. Entropy and diversity: the axiomatic approach. Cambridge university press, Cambridge

(UK), 2021.

[17] A. Mesoudi. Cultural Evolution: How Darwinian theory can explain human culture and synthesize

the social sciences. University of Chicago Press, Chicago (IL US), 2011.

[18] L. Steels, ed. Experiments in cultural language evolution. John Benjamins, Amsterdam (NL), 2012.
<scratchpad>
- Main topic: Research Centre MOEX at Inria Centre at Université Grenoble Alpes
- Key points:
  - Focus on perception, cognition, and interaction
  - Data and knowledge representation and processing
  - Research program on knowledge and belief representation semantics
  - Application domains: perception, cognition, and interaction
  - Partnerships and cooperations with other institutions
  - Dissemination of research through publications, conferences, and popularization
- Structure: The document is divided into sections, including activity report, project-team, contents, application domains, highlights, new software, new results, partnerships and cooperations, dissemination, and scientific production
- Potential artifacts to ignore: Links, HTML tags, and other web-related elements
</scratchpad>

After analyzing the document, I have identified the main topic and key points:

<final_summary>
The Research Centre MOEX at Inria Centre at Université Grenoble Alpes focuses on perception, cognition, and interaction, with a specific emphasis on data and knowledge representation and processing. The research program explores knowledge and belief representation semantics, and has applications in various domains. The centre has partnerships and cooperations with other institutions, and disseminates its research through publications, conferences, and popularization. The centre has made significant progress in recent years, with notable achievements in cultural knowledge evolution, data interlinking, and ontology matching.
</final_summary>
The Research Centre MOEX at Inria Centre at Université Grenoble Alpes focuses on perception, cognition, and interaction, with a specific emphasis on data and knowledge representation and processing. The research program explores knowledge and belief representation semantics, and has applications in various domains. The centre has partnerships and cooperations with other institutions, and disseminates its research through publications, conferences, and popularization. The centre has made significant progress in recent years, with notable achievements in cultural knowledge evolution, data interlinking, and ontology matching.