City Research Online

Abstract

This paper presents an enhancement of OWL2Vec4OA for ontology alignment, focusing on regression-based local matching. The enhancement integrates Sentence-BERT (SBERT) and Word2Vec embeddings, each combined with lexical and URI-based features. Different fusion strategies—concatenation, averaging, and merging—are explored to create richer embedding representations. The regression model leverages these hybrid embeddings to improve similarity prediction for local ontology matching. Regression models using these hybrid embeddings are evaluated with Hits@K and Mean Reciprocal Rank (MRR) metrics.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Publisher Keywords:	Ontology alignment, Machine Learning, Knowledge Graph Embeddings
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

Export

Downloads