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

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.
Type de document :
Communication dans un congrès
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015), Jan 2015, Austin, Texas, United States
Liste complète des métadonnées


https://hal.inria.fr/hal-01113463
Contributeur : Manuel Atencia Arcas <>
Soumis le : jeudi 5 février 2015 - 15:03:45
Dernière modification le : mercredi 14 décembre 2016 - 01:08:45
Document(s) archivé(s) le : mercredi 6 mai 2015 - 10:25:15

Fichier

al-bakri.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01113463, version 1

Collections

Citation

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>

Partager

Métriques

Consultations de
la notice

360

Téléchargements du document

248