Section: New Results

Ontology networks

Dealing with the semantic web, we are interested in ontology networks, i.e., sets of distributed ontologies that have to work together. One way for these systems to interact consists of exchanging queries and answers. For that reason, we pay particular attention to query systems.

Path queries and μ-calculus

Participants : Melisachew Wudage Chekol [Contact] , Jérôme Euzenat, Pierre Genevès, Nabil Layaïda.

Querying the semantic web is mainly done through SPARQL [15] . One of its extensions, PSPARQL (Path SPARQL ) provides queries with paths of arbitrary length. We study the static analysis of queries written in this language with techniques based on μ-calculus interpretation that have been used for XPATH . We have more specifically considered PSPARQL query containment: determining whether, for any graph, the answers to a query are contained in those of another query [18] [14] . To that extent, we proposed an encoding of RDF graphs as transition systems and PSPARQL queries as μ-calculus formulas. We then reduce the containment problem to testing satisfiability in the logic.

This work is part of the PhD of Melisachew Wudage Chekol, co-supervised with Nabil Layaïda (WAM ).

Trust in peer-to-peer semantic systems

Participants : Manuel Atencia [Contact] , Jérôme Euzenat, Marie-Christine Rousset.

In a semantic peer-to-peer network, peers use separate ontologies and rely on alignments between their ontologies for translating queries. Nonetheless, alignments may be incorrect --unsound or incomplete-- and generate flawed translations, thus leading to unsatisfactory answers. We have put forward a trust mechanism that can assist peers to select those peers in the network that are better suited to answer their queries [8] . The trust that a peer has towards another peer depends on a specific query and represents the probability that the latter peer will provide a satisfactory answer. In order to compute trust, we exploit both alignments and peers' direct experience, and perform Bayesian inference. We have implemented our technique and conducted an evaluation. Experimental results showed that trust values converge as more queries are sent and answers received. Furthermore, the use of trust is shown to improve both precision and recall of query answers.

This work has been developed in collaboration with Marie-Christine Rousset (LIG ) in tge context of the DataRing project (see § 8.1.2 ).