Towards Learning Commonalities in SPARQL

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 (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 lggs.
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https://hal.inria.fr/hal-01508720
Contributor : François Goasdoué <>
Submitted on : Friday, April 14, 2017 - 3:23:57 PM
Last modification on : Thursday, November 15, 2018 - 11:58:50 AM
Long-term archiving on: Saturday, July 15, 2017 - 3:58:33 PM

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  • HAL Id : hal-01508720, version 1

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Sara El Hassad, François Goasdoué, Hélène Jaudoin. Towards Learning Commonalities in SPARQL. Extended Semantic Web Conference (ESWC), May 2017, Portoroz, Slovenia. ⟨hal-01508720v1⟩

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