Born primarily as means to model knowledge, ontologies have successfully been exploited to enable knowledge exchange among people, organizations and software agents. However, because of strong subjectivity of ontology modeling, a matching process is necessary in order to lead ontologies into mutual agreement and obtain the relative alignment, i.e., the set of correspondences among them. The aim of this paper is to propose a memetic algorithm to perform an automatic matching process capable of computing a suboptimal alignment between two ontologies. To achieve this aim, the ontology alignment problem has been formulated as a minimum optimization problem characterized by an objective function depending on a fuzzy similarity. As shown in the performed experiments, the memetic approach results more suitable for ontology alignment problem than other evolutionary techniques such as genetic algorithms.
|Title of host publication||Proceedings of the 2011 IEEE International Conference on Fuzzy Systems (FUZZ), 27-30 June 2011, Taipei, Taiwan|
|Place of Publication||Piscataway|
|Publisher||Institute of Electrical and Electronics Engineers|
|Publication status||Published - 2011|
Acampora, G., Vitiello, A., Loia, V., Avella, P., & Salerno, S. (2011). Improving ontology alignment through memetic algorithms. In Proceedings of the 2011 IEEE International Conference on Fuzzy Systems (FUZZ), 27-30 June 2011, Taipei, Taiwan (pp. 1783-1790). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/FUZZY.2011.6007517