Learning Commonalities in SPARQL

Sara El Hassad (1) , François Goasdoué (1) , Hélène Jaudoin (1)

1 SHAMAN - Symbolic and Human-centric view of dAta MANagement

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.

Mots clés: RDF; RDFS; SPARQL; Least General Generalization

Domaines: Intelligence artificielle [cs.AI]; Base de données [cs.DB]; Web

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