An experimental analysis on evolutionary ontology meta-matching

Abstract

Every year, new ontology matching approaches have been published to address the heterogeneity problem in ontologies. It is well known that no one is able to stand out from others in all aspects. An ontology meta-matcher combines different alignment techniques to explore various aspects of heterogeneity to avoid the alignment performance being restricted to some ontology characteristics. The meta-matching process consists of several stages of execution, and sometimes the contribution/cost of each algorithm is not clear when evaluating an approach. This article presents the evaluation of solutions commonly used in the literature in order to provide more knowledge about the ontology meta-matching problem. Results showed that the more characteristics of the entities that can be captured by similarity measures set, the greater the accuracy of the model. It was also possible to observe the good performance and accuracy of local search-based meta-heuristics when compared to global optimization meta-heuristics. Experiments with different objective functions have shown that semi-supervised methods can shorten the execution time of the experiment but, on the other hand, bring more instability to the result.

Solving Ontology Metamatching Problem through Improved Multiobjective Particle Swarm Optimization Algorithm

“…Among them, selection is of importance for the integration, for some contradictory results are not possible to be integrated. Since only using a single similarity measure fails to ensure the confidence on all heterogeneous scenarios, various similarity measures are integrated to obtain a satisfactory alignment [8]. Ontology metamatching problem is aimed at how to choose the decent similarity measures, assign appropriate weights for them, and how to verify the alignment by removing the incorrect correspondences to enhance the quality of matching results, which is commonly a complex optimization problem with many local optima [9].…”