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Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings

2018-05-31Code Available1· sign in to hype

Ernesto Jimenez-Ruiz, Asan Agibetov, Matthias Samwald, Valerie Cross

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Abstract

Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In the paper we present an approach that combines a lexical index, a neural embedding model and locality modules to effectively divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed methods are adequate in practice and can be integrated within the workflow of state-of-the-art systems.

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