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 able stand out from others all aspects. An meta-matcher combines different alignment techniques explore various aspects of avoid performance being restricted some characteristics. The meta-matching process consists several stages execution, and sometimes contribution/cost each algorithm not clear when evaluating an approach. This article presents evaluation solutions commonly used literature order provide more knowledge about problem. Results showed characteristics entities can be captured by similarity measures set, greater accuracy model. was also possible observe good local search-based meta-heuristics compared global optimization meta-heuristics. Experiments with objective functions shown semi-supervised methods shorten execution time experiment but, on other hand, bring instability result.