Ontology Alignment Using Web Linked Ontologies as Background Knowledge

Thomas Hecht 1 Patrice Buche 2, 3 Juliette Dibie 4 Liliana Ibanescu 4 Cássia Trojahn dos Santos 5
2 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
5 EXMO - Computer mediated exchange of structured knowledge
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : This paper proposes an ontology matching method for aligning a source ontology with target ontologies already published and linked on the Linked Open Data (LOD) cloud. This method relies on the refinement of a set of input alignments generated by existing ontology matching methods. Since the ontologies to be aligned can be expressed in several representation languages with different levels of expressiveness and the existing ontology matching methods can only be applied to some representation languages, the first step of our method consists in applying existing matching methods to as many ontology variants as possible. We then propose to apply two main strategies to refine the initial alignment set: the removal of different kinds of ambiguities between correspondences and the use of the links published on the LOD. We illustrate our proposal in the field of life sciences and environment.
Type de document :
Chapitre d'ouvrage
Fabrice Guillet; Bruno Pinaud; Gilles Venturini. Advances in Knowledge Discovery and Management., 665, Springer, pp.207-227, 2017, Studies in Computational Intelligence, 978-3-319-45762-8. <10.1007/978-3-319-45763-5_11>
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https://hal-agroparistech.archives-ouvertes.fr/hal-01508810
Contributeur : Juliette Dibie <>
Soumis le : vendredi 14 avril 2017 - 17:31:02
Dernière modification le : lundi 24 avril 2017 - 14:45:29

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Thomas Hecht, Patrice Buche, Juliette Dibie, Liliana Ibanescu, Cássia Trojahn dos Santos. Ontology Alignment Using Web Linked Ontologies as Background Knowledge. Fabrice Guillet; Bruno Pinaud; Gilles Venturini. Advances in Knowledge Discovery and Management., 665, Springer, pp.207-227, 2017, Studies in Computational Intelligence, 978-3-319-45762-8. <10.1007/978-3-319-45763-5_11>. <hal-01508810>

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