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Context-aware instance matching through graph embedding in lexical semantic space

Assi Ali, Mcheick Hamid et Dhifli Wajdi. (2019). Context-aware instance matching through graph embedding in lexical semantic space. Dans : 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems , 9-11 july 2019, Graz, Austria.

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URL officielle: http://doi.org/10.1007/978-3-030-22999-3_37

Résumé

Instance matching is one of the processes that facilitate the integration of independently designed knowledge bases. It aims to link co-referent instances with an owl:sameAs connection to allow knowledge bases to complement each other. In this work, we present VDLS, an approach for automatic alignment of instances in RDF knowledge base graphs. VDLS generates for each instance a virtual document from its local description (i.e., data-type properties) and instances related to it through object-type properties (i.e., neighbors). We transform the instance matching problem into a document matching problem and solve it by a vector space embedding technique. We consider the pre-trained word embeddings to assess words similarities at both the lexical and semantic levels. We evaluate our approach on multiple knowledge bases from the instance track of OAEI. The experiments show that VDLS gets prominent results compared to several state-of-the-art existing approaches.

Type de document:Matériel de conférence (Non spécifié)
Date:19 Août 2019
Identifiant unique:10.1007/978-3-030-22999-3_37
Sujets:Sciences naturelles et génie > Sciences mathématiques > Informatique
Département, module, service et unité de recherche:Départements et modules > Département d'informatique et de mathématique
Liens connexes:
Mots-clés:data linking, instance matching, RDF graph, semantic web
Déposé le:10 nov. 2020 02:27
Dernière modification:10 nov. 2020 02:27
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