2019-01384 - PhD Position F/M Evolving ontologies through communication [PHD Campaign 2019 - Campagne Doctorants Grenoble Rhône-Alpes]

Contract type : Public service fixed-term contract

Level of qualifications required : Graduate degree or equivalent

Fonction : PhD Position

About the research centre or Inria department

Grenoble Rhône-Alpes Research Center groups together a few less than 800 people in 35 research teams and 9 research support departments.

Staff is localized on 5 campuses in Grenoble and Lyon, in close collaboration with labs, research and higher education institutions in Grenoble and Lyon, but also with the economic players in these areas.

Present in the fields of software, high-performance computing, Internet of things, image and data, but also simulation in oceanography and biology, it participates at the best level of international scientific achievements and collaborations in both Europe and the rest of the world.

Context

The work will be carried out in the mOeX team common to INRIA & LIG. mOeX is dedicated to study knowledge evolution through adaptation. It gather permanent researchers who have taken an active part these past 15 years in the development of the semantic web and more specifically ontology matching.

 

Assignment

Ontologies are representations of entities that can be found in the world. As the world and our standpoint on it change, ontologies cannot remain static. We aim at evolving ontologies while they are used by agents for communicating about the world they inhabit. This may be achieved by exchanging pieces of ontologies or by adapting those ontologies that prevent efficient communication.

These problems may be approached either theoretically or experimentally, through the framework of cultural evolution [Mesoudi 2006]. Experimental cultural evolution provides a population of agents with interaction games that are played randomly. In reaction to the outcome of such games, agents adapt their knowledge. It is possible to test hypotheses by precisely crafting the rules used by agents in games and observing the consequences.

Our ambition is to understand and develop general mechanisms by which a society evolves its knowledge. For that purpose, we adapt the successful cultural language evolution approach [Steels, 2012] to the evolution of the way agents represent knowledge [Euzenat, 2014; Anslow & Rovatsos, 2015; Chocron & Schorlemmer, 2016].

We have applied this approach to ontology alignment repair, i.e. the improvement of incorrect alignments [Euzenat, 2014; 2017]. We have performed experiments in which agents react to mistakes in ontology alignments —expressing relations across ontology concepts [Euzenat & Shvaiko, 2013]. Agents only know about their ontologies and alignments with others and they act in a fully decentralised way. We have shown that cultural repair is able to converge towards successful communication through improving the objective correctness of alignments.

This thesis proposal aims at developing the acquisition and evolution of ontologies through this approach more systematically. This covers learning ontologies from their environment, using them for communicating with others and evolving them through communication. Besides assertions across their ontologies, e.g. alignments, agents may exchange assertional knowledge, e.g. class membership of an individual, or terminological axioms, e.g. class subsumption or disjointness.

This involves designing new knowledge games and operators that agents use to adapt their knowledge with respect to the environment and interaction with others. This should contribute establishing if agents are able to reach perfect understanding, if they are able to describe their environment at the level of precision they perceive it or if they are able to develop all the same or logically equivalent knowledge.

In addition to study operators independently, we aim at combining them in order to obtain pluripotent agents able to evolve their knowledge through playing to different games.

This work is part of an ambitious program towards what we call cultural knowledge evolution. Its results may be of experimental or theoretical nature and it may provide practical, e.g. new adaptation operators, or methodological, e.g. better experimental procedures, contributions.

References:

[Anslow & Rovatsos, 2015] Michael Anslow, Michael Rovatsos, Aligning experientially grounded ontologies using language games, Proc. 4th international workshop on graph structure for knowledge representation, Buenos Aires (AR), pp15-31, 2015 [DOI:10.1007/978-3-319-28702-7_2]
[Chocron & Schorlemmer, 2016] Paula Chocron, Marco Schorlemmer, Attuning ontology alignments to semantically heterogeneous multi-agent interactions, Proc. 22nd European Conference on Artificial Intelligence, Der Haague (NL), pp871-879, 2016 [DOI:10.3233/978-1-61499-672-9-871]
[Euzenat & Shvaiko, 2013] Jérôme Euzenat, Pavel Shvaiko, Ontology matching, 2nd edition, Springer-Verlag, Heildelberg (DE), 2013
[Euzenat, 2014] Jérôme Euzenat, First experiments in cultural alignment repair (extended version), in: Proc. 3rd ESWC workshop on Debugging ontologies and ontology mappings (WoDOOM), Hersounisos (GR), LNCS 8798:115-130, 2014 ftp://ftp.inrialpes.fr/pub/exmo/publications/euzenat2014c.pdf
[Euzenat, 2017] Jérôme Euzenat, Communication-driven ontology alignment repair and expansion, in: Proc. 26th International joint conference on artificial intelligence (IJCAI), Melbourne (AU), pp185-191, 2017 ftp://ftp.inrialpes.fr/pub/moex/papers/euzenat2017a.pdf
[Mesoudi 2006] Alex Mesoudi, Andrew Whiten, Kevin Laland, Towards a unfied science of cultural evolution, Behavioral and brain sciences 29(4):329–383, 2006 http://alexmesoudi.com/s/Mesoudi_Whiten_Laland_BBS_2006.pdf
[Steels, 2012] Luc Steels (ed.), Experiments in cultural language evolution, John Benjamins, Amsterdam (NL), 2012

Links:

 


 

Skills

Qualification: Master or equivalent in computer science.

Researched skills:

  • Curiosity and openness.
  • Interaction with other researchers.
  • Autonomous researcher.
  • Taste for experimentation.
  • Knowledge of multi-agent simulation and/or logic not required but a plus.
  • Innovative.

Language: working English (written and spoken).

 

Benefits package

  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
  • Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage

Remuneration

Salary (before taxes) : 1982€ gross/month for 1st and 2nd year. 2085€ gross/month for 3rd year.