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Communication Dans Un Congrès Année : 2020

Ontology Matching Using Convolutional Neural Networks

Appariement d'ontologies à l'aide de réseaux de neurones convolutionnels

Alexandre Bento
Amal Zouaq
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Michel Gagnon
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Résumé

In order to achieve interoperability of information in the context of the Semantic Web, it is necessary to find effective ways to align different ontologies. As the number of ontologies grows for a given domain, and as overlap between ontologies grows proportionally, it is becoming more and more crucial to develop accurate and reliable techniques to perform this task automatically. While traditional approaches to address this challenge are based on string metrics and structure analysis, in this paper we present a methodology to align ontologies automatically using machine learning techniques. Specifically, we use convolutional neural networks to perform string matching between class labels using character embeddings. We also rely on the set of superclasses to perform the best alignment. Our results show that we obtain state-of-the-art performance on ontologies from the Ontology Alignment Evaluation Initiative (OAEI). Our model also maintains good performance when tested on a different domain, which could lead to potential cross-domain applications.
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Dates et versions

hal-04326319 , version 1 (12-12-2023)

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  • HAL Id : hal-04326319 , version 1

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Alexandre Bento, Amal Zouaq, Michel Gagnon. Ontology Matching Using Convolutional Neural Networks. Twelfth Language Resources and Evaluation Conference (LREC 2020), May 2020, Marseille, France. pp.5648-5653. ⟨hal-04326319⟩
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