Datalog revisited for reasoning in linked data

Marie-Christine Rousset 1, 2 Manuel Atencia 3 Jérôme David 3 Fabrice Jouanot 1 Olivier Palombi 4, 5 Federico Ulliana 6, 7
1 SLIDE - ScaLable Information Discovery and Exploitation [Saint Martin d’Hères]
LIG - Laboratoire d'Informatique de Grenoble
2 IUF - Institut Universitaire de France
3 MOEX - Evolution de la connaissance
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
4 LADAF - Laboratoire d'Anatomie des Alpes Françaises
5 UGA - Université Grenoble Alpes
6 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
7 UM - Université de Montpellier
Abstract : Linked Data provides access to huge, continuously growing amounts of open data and ontologies in RDF format that describe entities, links and properties on those entities. Equipping Linked Data with inference paves the way to make the Semantic Web a reality. In this survey, we describe a unifying framework for RDF ontologies and databases that we call deductive RDF triplestores. It consists in equipping RDF triplestores with Datalog inference rules. This rule language allows to capture in a uniform manner OWL constraints that are useful in practice, such as property transitivity or symmetry, but also domain-specific rules with practical relevance for users in many domains of interest. The expressivity and the genericity of this framework is illustrated for modeling Linked Data applications and for developing inference algorithms. In particular, we show how it allows to model the problem of data linkage in Linked Data as a reasoning problem on possibly decentralized data. We also explain how it makes possible to efficiently extract expressive modules from Semantic Web ontologies and databases with formal guarantees, whilst effectively controlling their succinctness. Experiments conducted on real-world datasets have demonstrated the feasibility of this approach and its usefulness in practice for data integration and information extraction.
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https://hal.inria.fr/hal-01661141
Contributor : Alain Monteil <>
Submitted on : Monday, December 11, 2017 - 5:14:14 PM
Last modification on : Friday, October 25, 2019 - 1:24:21 AM

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Marie-Christine Rousset, Manuel Atencia, Jérôme David, Fabrice Jouanot, Olivier Palombi, et al.. Datalog revisited for reasoning in linked data. Giovambattista Ianni; Domenico Lembo; Leopoldo Bertossi; Wolfgang Faber; Birte Glimm; Georg Gottlob; Steffen Staab. Reasoning Web. Semantic Interoperability on the Web. Reasoning Web 2017, LNCS (10370), Springer Verlag, pp.121-166, 2017, Reasoning Web International Summer School, 978-3-319-61032-0. ⟨10.1007/978-3-319-61033-7_5⟩. ⟨hal-01661141⟩

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