Abstract
Finding the commonalities between descriptions of data or knowledge is a foundational reasoning problem of Machine Learning, which amounts to computing a least general generalization (\({\mathtt {lgg}}\)) of such descriptions. We revisit this old problem in the popular conjunctive fragment of SPARQL, a.k.a. Basic Graph Pattern Queries (BGPQs). In particular, we define this problem in all its generality by considering general BGPQs, while the literature considers unary tree-shaped BGPQs only. Further, when ontological knowledge is available as RDF Schema constraints, we take advantage of it to devise much more pregnant \(\mathtt {lgg}\)s.
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El Hassad, S., Goasdoué, F., Jaudoin, H. (2017). Towards Learning Commonalities in SPARQL. In: Blomqvist, E., Hose, K., Paulheim, H., Ławrynowicz, A., Ciravegna, F., Hartig, O. (eds) The Semantic Web: ESWC 2017 Satellite Events. ESWC 2017. Lecture Notes in Computer Science(), vol 10577. Springer, Cham. https://doi.org/10.1007/978-3-319-70407-4_8
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DOI: https://doi.org/10.1007/978-3-319-70407-4_8
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