Enhancing Similarity Feature Construction via Genetic Programmings for Ontology Matching

Ontology serves as a fundamental technique within the Semantic Web by offering a structured framework for knowledge representation. However, its practical use is hindered by the entity heterogeneity problem, which re-sults from diverse representations of entities. Ontology matching identifies correspondences between entities with the same meaning in different ontologies, using Similarity Features (SFs) to measure entity similarity from various perspectives. Although Genetic Programming (GP) has shown promise in constructing SFs for ontology matching, its bloating issue reduces the matching accuracy and efficiency. To address this issue, this work designs a new SF construction framework, which incorporates a multi-objective SF building followed by a single-objective SF con-struction process. This two-stage architecture enables the exploration of diverse and high-quality SF combinations during the initial phase, while subsequently focusing on fine-tuning the most promising SFs through specialized op-timization in the second phase. The experiments on OAEI’s Conference dataset show that our method significantly outperforms state-of-the-art matching techniques, achieving higher accuracy and better efficiency.