Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-Based Modules
Ernesto Jiménez-Ruiz, Asan Agibetov, Jiaoyan Chen, Matthias Samwald, Valerie V. Cross · Frontiers in artificial intelligence and applications · 2020
Large ontologies still pose serious challenges to stateof-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.