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Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules

Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599, Agibetov, A., Chen, J. , Samwald, M. & Cross, V. (2020). Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules. Paper presented at the ECAI 2020, 8-12 Jun 2020, Santiago, Chile. doi: 10.3233/FAIA200167


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.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: The final publication will be available at IOS Press through
Publisher Keywords: Knowledge representation; Semantic Web; Semantic embeddings
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
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