2020/02/25 by Ernesto Jiménez-Ruiz, Asan Agibetov, Jiménez-Ruiz, Ernesto +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Biomedical Text Mining and Ontologies #Data Quality and Management #FOS: Computer and information sciences #I.2 #Semantic Web and Ontologies #cs.AI
paper · pdf · doi:10.48550/arxiv.2003.05370
Accepted to the 24th European Conference on Artificial Intelligence (ECAI 2020). arXiv admin note: text overlap with arXiv:1805.12402
arxiv created 2020/02/25 · openalex publication_date 2020/02/25 · arxiv updated 2020/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
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.