Collaborative Ontology Matching With Dual Population Genetic Programming and Active Meta-Learning

Ontology provides a structured language to encapsulate domain-specific knowledge and harmonize diverse data. Ontology matching (OM) identifies similar entities in distinct ontologies, facilitating knowledge integration and information exchange. Similarity features (SFs) are crucial for OM by measuring entity resemblance, but noisy and redundant features can obscure relevant ones, reducing matching quality. To improve the accuracy of matching results, we propose a dual population genetic programming (GP) with an active meta-learning to build a high-quality SF, which owns three novel components. First, a dual population GP is developed to construct high-level SF with a two-layer individual representation, a dual population based co-evolutionary mechanism, and a novel fitness function based on partial standard alignment (PSA). Second, a new active learning model is presented to update the PSA through an efficient interactive procedure, guiding the algorithm toward building more reliable SFs. Finally, a weighted random forest meta-learning model is designed to train the expert vote aggregation model with their historical behaviors, and fine-tunes the model's performance with a compact genetic algorithm. Experimental results on the Ontology Alignment Evaluation Initiative's interactive matching tasks demonstrate that our method consistently achieves higher accuracy and better efficiency compared to advanced matching techniques across various expert error rates.