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

Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599, Agibetov, A., Samwald, M. and Cross, V. (2018). Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings. .

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.

Publication Type: Monograph (Technical Report)
Additional Information: ontology alignment, divide and conquer, module extraction, partitioning,neural embedding model, lexical index, matching subtasks
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
Related URLs:
Date Deposited: 03 Oct 2019 13:39
URI: https://openaccess.city.ac.uk/id/eprint/22927
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