Learning Commonalities in SPARQL

Sara El Hassad 1 François Goasdoué 1 Hélène Jaudoin 1
1 SHAMAN - Symbolic and Human-centric view of dAta MANagement
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : Finding the commonalities between descriptions of data or knowledge is a foundational reasoning problem of Machine Learning. It was formalized in the early 70's as computing a least general generalization (lgg) of such descriptions. We revisit this well-established problem in the SPARQL query language for RDF graphs. In particular, and by contrast to the literature, we address it for the entire class of conjunc-tive SPARQL queries, a.k.a. Basic Graph Pattern Queries (BGPQs), and crucially, when background knowledge is available as RDF Schema ontological constraints, we take advantage of it to devise much more precise lggs, as our experiments on the popular DBpedia dataset show.
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Communication dans un congrès
International Semantic Web Conference (ISWC), Oct 2017, Vienna, Austria
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Contributeur : François Goasdoué <>
Soumis le : mardi 8 août 2017 - 11:09:43
Dernière modification le : jeudi 10 août 2017 - 01:06:50

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  • HAL Id : hal-01572691, version 1

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Sara El Hassad, François Goasdoué, Hélène Jaudoin. Learning Commonalities in SPARQL. International Semantic Web Conference (ISWC), Oct 2017, Vienna, Austria. 〈hal-01572691〉

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