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We divide, you conquer: From large-scale ontology alignment to manageable subtasks with a lexical index and neural embeddings

Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599, Agibetov, A., Samwald, M. and Cross, V. (2018). We divide, you conquer: From large-scale ontology alignment to manageable subtasks with a lexical index and neural embeddings. CEUR Workshop Proceedings, 2288, pp. 13-24.

Abstract

Large ontologies still pose serious challenges to state-of-the-art on-tology alignment systems. In this paper we present an approach that combines alexical index, a neural embedding model and locality modules to effectively di-vide an input ontology matching task into smaller and more tractable matchingsubtasks. We have conducted a comprehensive evaluation using the datasets ofthe Ontology Alignment Evaluation Initiative. The results are encouraging andsuggest that the proposed methods are adequate in practice and can be integratedwithin the workflow of state-of-the-art systems.

Publication Type: Article
Additional Information: © The Authors.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: https://openaccess.city.ac.uk/id/eprint/22954
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