Matching heterogeneous ontologies via transfer learning

Ontology matching plays a pivotal role in aligning entities across diverse ontologies, enhancing interoperability and data exchange. Similarity features are the foundation of ontology matching, which assess entity similarities from multiple perspectives. Selecting relevant similarity features tailored to specific matching tasks is crucial due to the intricate heterogeneity of entities. However, traditional similarity feature selection methods face significant limitations, such as reliance on extensively curated datasets, the need for extensive human intervention, and inconsistencies in handling complex high-dimensional data structures. To address these challenges, we develop a novel transfer learning approach for automatic similarity feature selection. Our approach includes a new transfer learning framework to enhance similarity feature selection for ontology matching, a novel similarity feature alignment extraction method utilizing mutual information to align feature subsets using an entropy-based metric, and a real-value compact genetic algorithm to adjust the feature space and train an ordered weighting aggregation-based classifier. We compared our method with advanced techniques on OAEI’s Benchmark and Bio-ML datasets. Experimental results show that our method significantly outperforms the competitors across various tasks. By automating similarity feature selection and reducing dependency on manual interventions, our method addresses the complexities of traditional approaches, enhancing the practical utility of ontology matching.