Adaptive Similarity Feature Construction for Ontology Matching via Multilayer Hybrid Genetic Programming

Ontology is a kernel technique of the semantic web, which defines concepts, properties, and their relationships to establish a shared understanding of domain knowledge. Ontology matching identifies semantically similar entities across different ontologies, which uses similarity features to measure their similarity from different perspectives. However, due to the complexity of the entity heterogeneity, no single similarity feature is universally effective. In recent years, genetic algorithms have proven effective in constructing similarity features for ontology matching, but their potential is limited by the reliance on default classification strategies, empirical determination of the number of high-level features, the requirement for manually selecting, combining these features, and tuning the associated combination parameters. To overcome these drawbacks, we propose a multi-layer hybrid genetic programming approach to automatically construct high-level similarity features. This approach includes three novel components. First, a new multi-layer individual representation is designed, which faciliates the algorithm to adaptively explore the search space of constructing high-level similarity features. Second, to enhance the search effectiveness, a new initialization method and a mutation operator are developed, which use a weight-based strategy to adaptively select and construct a more diverse set of similarity features. Third, a compact genetic algorithm-based optimizer is designed to refine the tree structures of elite individuals. The experimental results on the ontology alignment evaluation initiative’s benchmark show that our algorithm can generate high-quality ontology matching results across various matching tasks, significantly outperforming the state-of-the-art ontology matching methods.