Jean-François Baget

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RDF is a knowledge representation language dedicated to the annotation of resources<lb>within the framework of the semantic web. Among the query languages for RDF, SPARQL<lb>allows querying RDF through graph patterns, i.e., RDF graphs involving variables. Other<lb>languages, inspired by the work in databases, use regular expressions for searching(More)
Simple conceptual graphs are considered as the kernel of most knowledge representation formalisms built upon Sowa's model. Reasoning in this model can be expressed by a graph homomorphism called projection, whose semantics is usually given in terms of positive, conjunctive, existential FOL. We present here a family of extensions of this model, based on(More)
In ∀∃-rules, the conclusion may contain existentially quantified variables, which makes reasoning tasks (as deduction) non-decidable. These rules have the same logical form as TGD (tuplegenerating dependencies) in databases and as conceptual graph rules. We extend known decidable cases by combining backward and forward chaining schemes, in association with(More)
We consider positive rules in which the conclusion may contain existentially quantified variables, which makes reasoning tasks (such as Deduction) undecidable. These rules have the same logical form as TGD (tuple-generating dependencies) in databases and as conceptual graph rules. The aim of this paper is to provide a clearer picture of the frontier between(More)
We establish complexities of the conjunctive query entailment problem for classes of existential rules (i.e. Tuple-Generating Dependencies or Datalog+/rules). Our contribution is twofold. First, we introduce the class of greedy bounded treewidth sets (gbts), which covers guarded rules, and their known generalizations, namely (weakly) frontier-guarded rules.(More)
Semantic consequence (entailment) in RDF is ususally computed using Pat Hayes Interpolation Lemma. In this paper, we reformulate this mechanism as a graph homomorphism known as projection in the conceptual graphs community. Though most of the paper is devoted to a detailed proof of this result, we discuss the immediate benefits of this reformulation: it is(More)
The algorithm presented here, BCC, is an enhancement of the well known Backtrack used to solve constraint satisfaction problems. Though most backtrack improvements rely on propagation of local informations, BCC uses global knowledge of the constraint graph structure (and in particular its biconnected components) to reduce search space, permanently removing(More)