Results of OWL2Vec4OA in the OAEI 2025

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.