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.
Lectures are on Wednesday from 9h45 to 12h45 (most of the time in room H202).
| Date | Title | Room | Lecturer |
| 22/11 | Knowledge, web, agents, etc. | H202 | JE |
| 29/11 | Ontology networks | H202 | JE |
| 06/12 | Belief revision | H202 | JE |
| 13/12 | Distributed query evaluation | H202 | JE |
| 20/12 | Social and cultural knowledge evolution | H202 | JE |
| 10/01 | Logics of knowledge | H208 | JE |
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.
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.
Indicative bibliography:
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.
Indicative bibliography:
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.
Indicative bibliography:
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.
Indicative bibliography:
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.
Indicative bibliography:
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.
Indicative bibliography:
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.