Improving the efficiency of NSGA-II based ontology aligning technology

摘要

There is evidence from Ontology Alignment Evaluation Initiative (OAEI) that ontology matchers do not necessarily find the same correct correspondences. Therefore, usually several competing matchers are applied to the same pair of entities in order to increase evidence towards a potential match or mismatch. How to select the proper matcher's alignments and efficiently tune them becomes one of the challenges in ontology matching domain. To this end, in this paper, we propose to use the Dynamic Alignment Candidates Selection Strategy and Metamodel to raise the efficiency of the process of using NSGA-II to optimize the ontology alignment by prescreening the less promising aligning results to be combined and individuals to be evaluated in the NSGA-II, respectively. The experiment results show that, comparing with the approach by using NSGA-II solely, the utilization of Dynamic Alignment Candidates Selection Strategy and Metamodel is able to highly reduce the time and main memory consumption of the tuning process while at the same time ensures the correctness and completeness of the alignments. Moreover, our proposal is also more efficient than the state-of-the-art ontology aligning systems.