Quotation Ferranti, Nicolas, De Souza, Jairo Francisco, Rosário Furtado Soares, Stênio Sã. 2021. An experimental analysis on evolutionary ontology meta-matching. Knowledge and Information Systems. 63 2919-2946.




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.


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  • Ontology meta-matching
  • Metaheuristic-based ontology matching
  • Evolutionary ontology matching
  • Semantic Web

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Knowledge and Information Systems
Citation Index SCI
WU-Journal-Rating new INF-A
Language English
Title An experimental analysis on evolutionary ontology meta-matching
Volume 63
Year 2021
Page from 2919
Page to 2946
Reviewed? Y
URL https://link.springer.com/article/10.1007/s10115-021-01613-0
DOI https://doi.org/10.1007/s10115-021-01613-0
Open Access Y
Open Access Link https://link.springer.com/content/pdf/10.1007/s10115-021-01613-0.pdf


Ferranti, Nicolas (Details)
De Souza, Jairo Francisco (Federal University of Juiz de Fora, Brazil)
Rosário Furtado Soares, Stênio Sã (Federal University of Juiz de Fora, Brazil)
Institute for Data, Process and Knowledge Management (AE Polleres) (Details)
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