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

Authors

  • Mustafa Al-Bakri University of Grenoble Alpes
  • Manuel Atencia University of Grenoble Alpes
  • Steffen Lalande Institut National de l’Audiovisuel
  • Marie-Christine Rousset University of Grenoble Alpes

DOI:

https://doi.org/10.1609/aaai.v29i1.9174

Keywords:

Linked Open Data data linkage forward and backward reasoning Datalog rules external queries

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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Published

2015-02-09

How to Cite

Al-Bakri, M., Atencia, M., Lalande, S., & Rousset, M.-C. (2015). Inferring Same-As Facts from Linked Data: An Iterative Import-by-Query Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9174