Cultural knowledge evolution deals with the evolution of knowledge representation in a group of agents. For that purpose, cooperating agents adapt their knowledge to the situations they are exposed to and the feedback they receive from others. We have applied it to ontology alignment repair, i.e. the improvement of incorrect alignments [Euzenat, 2017] and ontology evolution [Bourahla et al., 2021]. We have shown that it converges towards successful communication through improving the intrinsic knowledge quality.
In our recent work on cultural evolution [van den Berg, 2021], each agent is assumed to have its own ontology, which models the knowledge about the world. After interacting with the community, agents will update or evolve their ontology to better communicate in the future. However, in more realistic communication settings, agents often choose to preserve their own world representation (which we may refer to as their standpoint), and rather adjust the way they interpret messages from other agents, i.e. they also model the other agent’s standpoint and the way both map.
We have recently introduced Standpoint Logic (SL) [Gómez Álvarez and Rudolph, 2021], a framework to model heterogeneous knowledge held by different agents. SL is a modal logic allowing agents to establish individual standpoints, which involves specifying constraints and relations. It is close to epistemic logic, but its simplified semantics allows it to support more expressive underlying languages [Gómez Álvarez et al., 2023] (usual in ontologies and knowledge bases). SL facilitates combining standpoints and establishing alignments between them.
Within this thesis topic, the aim is to model cultural evolution with standpoint logic representations or to define cultural evolution protocols suitable for being expressed in standpoint logic. The focus is thus centred on studying the evolution of cultures that encompass a diversity of viewpoints.
References:
[Bourahla et. al., 2021] Yasser Bourahla, Manuel Atencia, Jérôme Euzenat, Knowledge improvement and diversity under interaction-driven adaptation of learned ontologies, Proc. 20th AAMAS, London (UK), pp242-250, 2021 https://moex.inria.fr/files/papers/bourahla2021a.pdf
[Euzenat, 2017] Jérôme Euzenat, Communication-driven ontology alignment repair and expansion, Proc. 26th IJCAI, Melbourne (AU), pp185-191, 2017 https://moex.inria.fr/files/papers/euzenat2017a.pdf
[Gómez Álvarez and Rudolph, 2021] Gómez Álvarez, L. and Rudolph, S. (2021). Standpoint Logic: Multi-
Perspective Knowledge Representation. Frontiers in Artificial Intelligence and Applications, 344:3–17.
[Gómez Álvarez et al., 2023] Gómez Álvarez, L., Rudolph, S., and Strass, H. (2023). Tractable diversity: scalable
multiperspective ontology management via standpoint EL. Proc. 32nd IJCAI, 2023.
[van den Berg, 2021] Line van den Berg, Cultural knowledge evolution in dynamic epistemic logic, Phd thesis, Université Grenoble Alpes, 2021 https://moex.inria.fr/files/theses/thesis-vandenberg.pdf
Links:
Qualification: Master or equivalent in computer science.
Researched skills:
Doctoral school: MSTII, Université Grenoble Alpes.
Advisor: Lucía Gómez Álvarez (Lucia:Gomez-Alvarez#inria.fr) and Jérôme Euzenat (Jerome:Euzenat#inria:fr).
Group: The work will be carried out in the mOeX team common to INRIA & LIG. mOeX is dedicated to study knowledge evolution through adaptation. It gathers researchers which have taken an active part these past 15 years in the development of the semantic web and more specifically ontology matching and data interlinking.
Place of work: The position is located at INRIA Montbonnot (near Grenoble) a main computer science research lab, in a stimulating research environment.
Hiring date: October 2025.
Duration: 36 months
Deadline: as soon as possible.
Contact: For further information, contact us.
Procedure: Contact us .
File: Provide Vitæ, motivation letter and references. It is very good if you can provide a Master report and we will ask for your marks in Master, so if you have them, you can join them.