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

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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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Dates and versions

hal-01113463 , version 1 (05-02-2015)

Identifiers

  • HAL Id : hal-01113463 , version 1

Cite

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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