Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules
This addresses scalability issues for ontology alignment systems, particularly benefiting domains reliant on large ontologies, though it appears incremental as it builds on existing methods.
The paper tackles the challenge of aligning large ontologies by dividing the task into smaller subtasks using a combination of neural embeddings and logic-based modules, achieving encouraging results in evaluations on standard datasets.
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.