Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix

The ultimate goal of semantic web (SW) is to implement mutual collaborations among ontology-based intelligent systems. To this end, it necessary integrate those domain-independent and cross-domain ontologies by finding the correspondences between their entities, which so-called ontology matching. improve quality alignment, in work, matching problem first defined as a sparse multi-objective optimization (SMOOP), then, evolutionary algorithm with relevance matrix (MOEA-RM) proposed address it. In particular, (RM) presented adaptively measure each individual’s genes objectives, applied MOEA’s initialization, crossover mutation ensure population’s sparsity speed up algorithm’s convergence. experiment verifies performance MOEA-RM comparing state-of-the-art techniques, experimental results show that able effectively different heterogeneity characteristics.