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DaRLing: A Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries

Published online by Cambridge University Press:  22 September 2020

ALESSIO FIORENTINO
Affiliation:
Department of Mathematics and Computer Science (DeMaCS), University of Calabria, Rende, Italy (e-mail: [email protected]) - https://www.mat.unical.it
JESSICA ZANGARI
Affiliation:
Department of Mathematics and Computer Science (DeMaCS), University of Calabria, Rende, Italy (e-mail: [email protected]) - https://www.mat.unical.it
MARCO MANNA
Affiliation:
Department of Mathematics and Computer Science (DeMaCS), University of Calabria, Rende, Italy (e-mail: [email protected]) - https://www.mat.unical.it

Abstract

The W3C Web Ontology Language (OWL) is a powerful knowledge representation formalism at the basis of many semantic-centric applications. Since its unrestricted usage makes reasoning undecidable already in case of very simple tasks, expressive yet decidable fragments have been identified. Among them, we focus on OWL 2 RL, which offers a rich variety of semantic constructors, apart from supporting all RDFS datatypes. Although popular Web resources - such as DBpedia - fall in OWL 2 RL, only a few systems have been designed and implemented for this fragment. None of them, however, fully satisfy all the following desiderata: (i) being freely available and regularly maintained; (ii) supporting query answering and SPARQL queries; (iii) properly applying the sameAs property without adopting the unique name assumption; (iv) dealing with concrete datatypes. To fill the gap, we present DaRLing, a freely available Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries. In particular, we describe its architecture, the rewriting strategies it implements, and the result of an experimental evaluation that demonstrates its practical applicability.

Type
Original Article
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

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This work has been partially supported by MISE under the project “S2BDW” (F/050389/01-03/X32) – Horizon 2020 PON I&C 2014-2020 and by Regione Calabria under the project “DLV LargeScale” (CUP J28C17000220006) – POR Calabria 2014-2020.

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