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Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-Based Modules
Ernesto Jiménez-Ruiz, Asan Agibetov, Jiaoyan Chen, Matthias Samwald, Valerie Cross
784 - 791
10.3233/FAIA200167
Research Article
Frontiers in Artificial Intelligence and Applications
Volume 325: ECAI 2020
Abstract

Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic-based modules to accurately 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 method is adequate in practice and can be integrated within the workflow of systems unable to cope with very large ontologies.

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