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 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.
CITATION STYLE
El Hassad, S., Goasdoué, F., & Jaudoin, H. (2017). Learning commonalities in SPARQL. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10587 LNCS, pp. 278–295). Springer Verlag. https://doi.org/10.1007/978-3-319-68288-4_17
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