Heterogeneous Ontology Alignment Extraction For E-Business Through Compact Co-Evolutionary Niching Genetic Algorithm
Xinglin Liu, Jianhui Lv · 2023
Ontologies provide a standardized approach to knowledge representation that can be shared across various domains. By extracting heterogeneous ontology alignment, E-businesses can efficiently exchange information and enhance communication, decision-making, and reduce data integration costs. In this study, we investigate the heterogeneous ontology alignment extraction problem for E-business, which aims to determine an optimal concept pair set with the highest f-measure value. Given the alignment extraction’s inherent complexity, we use a Genetic Algorithm (GA) to address it. In particular, we first model the HOAEP as a multi-modal problem with sparse solutions and then propose a Compact Co-Evolutionary Niching Genetic Algorithm (CCNGA) to address it. CCNGA first employs probability distribution estimation to simplify population representation, and then uses three evolutionary strategies to simultaneously search for the global optimum. The experimental testing cases include OAEI’s Conference track and three real E-business ontologies, and T-test results demonstrate that CCNGA significantly outperforms other state-of-the-art ontology alignment extraction techniques.