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.
@article{arxiv.2003.05370,
title = {Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules},
author = {Ernesto Jiménez-Ruiz and Asan Agibetov and Jiaoyan Chen and Matthias Samwald and Valerie Cross},
journal= {arXiv preprint arXiv:2003.05370},
year = {2020}
}
Comments
Accepted to the 24th European Conference on Artificial Intelligence (ECAI 2020). arXiv admin note: text overlap with arXiv:1805.12402