Semi-Automatic Ontology Matching Based on Interactive Compact Genetic Algorithm
Xingsi Xue, Chaofan Yang, Guojun Mao, Hai Jiang Zhu · International Journal of Pattern Recognition and Artificial Intelligence · 2022
Ontology matching is able to identify the entity correspondences between two heterogeneous ontologies, which is an effective method to solve the data heterogeneous problem on the Semantic Web. Traditional fully-automatic ontology matching techniques suffer from the limitation of similarity measure, whose alignment’s quality cannot be ensured. To overcome this drawback, in this work, an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique is proposed, which utilizes both the compact encoding mechanism and expert interacting mechanism to improve the algorithm’s performance and the alignment’s quality. In addition, an optimization model is established to formally define the ontology entity matching problem, and an efficient interacting strategy is proposed, which is able to reduce the expert’s workload and maximize his working value. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test our proposal’s performance. The experimental results show that our approach is able to make use of the expert knowledge to improve the alignment’s quality, and it also outperforms OAEI’s participants.