University Grenoble Alpes, 2nd year Master of science in informatics (MoSIG), specialty Artificial intelligence and the web, Semantic web course

Semantics of Distributed Knowledge

Jérôme Euzenat (Jerome : Euzenat # inria : fr)
Course web site
Fundamentals of Data Processing and Distributed Knowledge


This is part of the "Fundamentals of Data Processing and Distributed Knowledge" course. Its starts from the observation that modern computing increasingly takes advantage of large amounts of distributed data and knowledge. This is grounded on theoretical principles borrowing to several fields of computer science such as programming languages, data bases, structured documentation, logic and artificial intelligence. The goal of this course is to present some of them, the problems that they solve and those that they uncover. The course considers three perspectives on data and knowledge: interpretation (what they mean), analysis (what they reveal) and processing (how can they be traversed efficiently and transformed safely).

The "Semantics of Distributed Knowledge" part offers a semantic perspective on distributed knowledge. Distributed knowledge may come from data sources using different ontologies on the semantic web, autonomous software agents learning knowledge or social robots interacting with different interlocutors. The course adopts a synthetic view on these and revolves around the question: how to approach the knowledge of a multiplicity of agents. It touches disciplines such as: knowledge representation, query evaluation, multi agent systems, logics and reasoning. The constant preoccupation of this course is how such disciplines can help and take advantage of distributed knowledge according to its semantics.

The course is organised around 5 topics. It first presents principles of the semantics of knowledge representation (RDF, OWL). Ontology alignments are then introduced to reduce the heterogeneity between distributed knowledge and their exploitation for answering federated queries is presented. A practical way for cooperating agents to evolve their knowledge is cultural knowledge evolution that is then illustrated. Finally, the course defines dynamic epistemic logics as a way to model the communication of knowledge and beliefs.

This opens perspectives to other types of activities such as belief revision and human-machine cooperation.

Place and time

Lectures are on Wednesday from 9h45 to 12h45 (most of the time in room H202).

Planning (2023–2024)

22/11Knowledge, web, agents, etc.H202JE
29/11Ontology networksH202JE
06/12Belief revisionH202JE
13/12Distributed query evaluationH202JE
20/12Social and cultural knowledge evolutionH202JE
10/01Logics of knowledgeH208JE

Lecture notes

The full course is documented by self-contained and comprehensive lecture notes that are available here. It is the main bibliographic item to consult. The indicative bibliography below is mostly aimed at going forward from the course.


The course is organised around 6 different topics corresponding to as many sessions.

Knowledge, web, agents, etc.

In which we start representing knowledge on computer, first independently, then together. Assessing meaning to this representation requires a semantics. This sometimes reveal that our knowledge is contradictory, redundant or misaligned.

  1. Distributed knowledge representation
    1. Motivations
    2. Occurence in the semantic web and modern AI
    3. Objectives of the course
  2. Expressing data with RDF: syntax and semantics
    1. RDF Graphs
    2. RDF Semantics
    3. Simple entailment
  3. Modelling knowledge with a simple ontology language
    1. A reduced ontology language
    2. Semantics
    3. Consistency, data and formula entailment

Indicative bibliography:

  • Notes of the KRR course
  • Grigoris Antoniou, Frank van Harmelen, A semantic web primer, The MIT press, 2004 (rev. 2008)
  • Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph, Foundations of semantic web technologies, Chapman & Hall/CRC, 2009
  • Networks of ontologies

    In which we lay bridges (alignments) between theories to address misunderstanding, ending with a network of related ontologies. We provide different semantics for such networks allowing to understand the benefit of alignments for interoperation. We can interpret the network as a whole or in the neighborhood of a particular agent.

    1. Connecting ontologies with alignments
      1. Ontology alignments
      2. Networks of ontologies
    2. Semantics of ontology alignments
      1. ADVANCED (not covered): Three alignment semantics
      2. The reduced alignment semantics
      3. Correspondence and formula entailment
      4. Alignment entailment entailment
    3. Semantics of networks of ontologies
      1. Semantics
      2. Overall consequences of alignment semantics
      3. Global and local consistency

    Indicative bibliography:

  • Jérôme Euzenat, Pavel Shvaiko, Ontology matching, Springer Verlag, Heidelberg (DE), 2007; 2nde edition, 2013
  • Distributed queries

    In which we cross these bridges, exploiting networks of ontologies and linked data, in order to carry back answers to queries. The relationship between query evaluation and semantics becomes clear.

    1. Queries and entailment
      1. Graph query language
      2. Query semantics
    2. Querying modulo ontologies
    3. Queries over several data sources
    4. The different distributed query semantics
    5. Dealing with heterogeneous sources

    Indicative bibliography:

  • Serge Abiteboul, Ioana Manolescu, Philippe Rigaux, Marie-Christine Rousset, Pierre Senellart, Data Integration, Chapter 9 of Web Data Management, Cambridge university press, Cambridge (UK), 2011
  • Philippe Adjiman, Philippe Chatalic, Francois Goasdoué, Marie-Christine Rousset, Laurent Simon, Distributed Reasoning in a Peer-to-Peer Setting: Application to the Semantic Web, Journal of Artificial Intelligence Research (JAIR) Volume 25. 2006.
  • Belief revision

    In which we face inconsistency, or the absence of model. It spreads to the whole network of ontologies and contaminates query results. Isolating maximal consistent sub-networks and repairing inconsistency reveal quite challenging. The framework of belief revision allows to approach dealing with inconsistency in a principled way.

    1. Inconsistency, isolation, repair
    2. Belief revisionPostulates for belief revision operators
      1. Revision operators
      2. AGM postulates for belief revision
      3. Contraction, update and base revision
      4. Partial meet revision
    3. Adaptation to ontologies and alignments
      1. Problems for the application to description logics
      2. Operators for description logic ontologies
      3. Alignment revision
    4. Revision operators for networks of ontologies
      1. Network of ontologies revision operators
      2. Network of ontologies revision postulates
      3. Local revision is not sufficient
      4. Partial-meet revision operators for networks of ontologies

    Indicative bibliography:

  • Jérôme Euzenat, Revision in networks of ontologies, Artificial intelligence 228:195-216, 2015
  • Faiq Miftakhul Falakh, Sebastian Rudolph, Kai Sauerwald, Semantic characterizations of general belief base revision, tech. rep. 2112.13557, arXiv, 2021
  • Eduardo Fermé, Sven Ove Hansson, Belief change: introduction and overview, Springer, Cham (CH), 2018
  • Social and cultural knowledge evolution

    In which relying on existing bridges between knowledge representations is not sufficient: common knowledge is grounded on common experience. We investigate how dynamic techniques of cultural evolution can be compatible with knowledge representation semantics.

    1. Cultural knowledge evolution
      1. Motivation
      2. History
      3. Replicator-interactor
    2. Methodology
    3. The cultural alignment repair game
      1. Setting
      2. Adaptation operators and modalities
      3. Measured properties
    4. First results
      1. Convergence
      2. Operator comparison
      3. Comparison with logical repair
      4. Non scalability
    5. Extensions
      1. Relaxation
      2. Expansion
      3. Strengthening
      4. Starting empty
      5. Population variants

    Indicative bibliography:

  • Alex Mesoudi, Cultural Evolution: How Darwinian theory can explain human culture and synthesize the social sciences, University of Chicago Press, Chicago (IL US), 2011
  • Luc Steels (ed.), Experiments in cultural language evolution, John Benjamins, Amsterdam (NL), 2012
  • Logics of knowledge

    In which these attempts are recast in decades of trying to capture knowledge into logic. Knowledge is too wild to be captured, but logic provides a solid tutor to grow along.

    1. Introduction to the semantics of modal logics
      1. Syntax
      2. Semantic structures and "pointed" models
      3. Ontology encoding
      4. Axiom schemata
    2. Epistemic-doxastic logic
      1. Syntax and axiomatics
      2. Baltag-Smets semantics
    3. Multi-agent epistemic logic
      1. Syntax and semantics
      2. Distributed and common knowledge
    4. Communication as action: dynamic epistemic logic
      1. Announcement and model transformation
    5. Example: modelling the alignment repair game
      1. Encoding ontologies as knowledge and alignments as belief
      2. Adaptation operators as announcements
      3. Logical properties of the alignment repair game

    Indicative bibliography:

  • Ronald Fagin, Joseph Halpern, Yoran Moses, Moshe Vardi, Reasoning about knowledge, The MIT press, Cambridge (MA US), 1995
  • Hans van Ditmarsch, Wiebe van der Hoek, Barteld Kool, Dynamic epistemic logic, Springer, Heidelberg (DE), 2018
  • Richard Zack, Boxes and diamonds: an open introduction to modal logic, 2020 https://bd.

    Previous exams

    Find below example, of recent previous exams with their corrections. Note that the 'theoretical questions', or 'course questions', are simply given as a sample so that you can see what kind of answers are expected. You can imagine others from the lecture notes.