Inferring Same-as Facts from Linked Data: An Iterative Import-by-Query Approach

Mustafa Al-Bakri 1, 2, 3, 4 Manuel Atencia 2, 3, 4, 5 Steffen Lalande 6 Marie-Christine Rousset 2, 3, 4, 7
1 LIG Laboratoire d'Informatique de Grenoble - HADAS
LIG - Laboratoire d'Informatique de Grenoble
2 Laboratoire d'Informatique de Grenoble
3 CNRS - Centre National de la Recherche Scientifique
4 UGA - Université Grenoble Alpes
5 EXMO - Computer mediated exchange of structured knowledge
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
6 INA - Institut National de l'Audiovisuel
7 IUF - Institut Universitaire de France
Abstract : In this paper we model the problem of data linkage in Linked Data as a reasoning problem on possibly decentralized data. We describe a novel import-by-query algorithm that alternates steps of sub-query rewriting and of tailored querying the Linked Data cloud in order to import data as specific as possible for inferring or contradicting given target same-as facts. Experiments conducted on a real-world dataset have demonstrated the feasibility of this approach and its usefulness in practice for data linkage and disambiguation.
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Mustafa Al-Bakri, Manuel Atencia, Steffen Lalande, Marie-Christine Rousset. Inferring Same-as Facts from Linked Data: An Iterative Import-by-Query Approach. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015), Jan 2015, Austin, Texas, United States. ⟨hal-01113463⟩

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