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Efficient reasoning on large and heterogeneous graphs

Maxime Buron 1
1 CEDAR - Rich Data Analytics at Cloud Scale
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France
Abstract : The Semantic Web offers knowledge representations, which allow to integrate heterogeneous data from several sources into a unified knowledge base. In this thesis, we investigate techniques for querying such knowledge bases. The first part is devoted to query answering techniques on a knowledge base, represented by an RDF graph subject to ontological constraints. Implicit information entailed by the reasoning, enabled by the set of RDFS entailment rules, has to be taken into account to correctly answer such queries. First, we present a sound and complete query reformulation algorithm for Basic Graph Pattern queries, which exploits a partition of RDFS entailment rules into assertion and constraint rules. Second, we introduce a novel RDF storage layout, which combines two well-known layouts. For both contributions, our experiments assess our theoretical and algorithmic results. The second part considers the issue of querying heterogeneous data sources integrated into an RDF graph, using BGP queries. Following the Ontology-Based Data Access paradigm, we introduce a framework of data integration under an RDFS ontology, using the Global-Local-As-View mappings, rarely considered in the literature. We present several query answering strategies, which may materialize the integrated RDF graph or leave it virtual, and differ on how and when RDFS reasoning is handled. We implement these strategies in a platform, in order to conduct experiments, which demonstrate the particular interest of one of the strategies based on mapping saturation. Finally, we show that mapping saturation can be extended to reasoning defined by a subset of existential rules.
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https://hal.inria.fr/tel-03107689
Contributor : Maxime Buron <>
Submitted on : Tuesday, January 12, 2021 - 6:14:52 PM
Last modification on : Tuesday, March 2, 2021 - 5:19:25 PM
Long-term archiving on: : Tuesday, April 13, 2021 - 6:57:09 PM

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Maxime Buron. Efficient reasoning on large and heterogeneous graphs. Artificial Intelligence [cs.AI]. École Polytechnique, 2020. English. ⟨tel-03107689⟩

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