Research – Paper 262

Learning Commonalities in SPARQL Sara El Hassad, Francois Goasdoue and Helene Jaudoin Research October 24, 2017, 15:30. Stolz 1 Download paper (preprint)

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 bycontrast to the literature, we address it for the entire class of conjunctive 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.