Evolutionary Ontology Matching Technique with User Involvement.

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 suffers from the limitation of similarity measure, whose alignment’s quality can not be ensured. To overcome this drawback, in this work, an Evolutionary Ontology Matching technique with User Involvement (EOM-UI) is proposed, which utilizes both the Compact Evolutionary Algorithm and user knowledge 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 to reduce the user’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 user knowledge to improve the alignment’s quality, and it also outperforms OAEI’s participants.