GFSOM: Genetic Feature Selection for Ontology Matching
Journal article, Peer reviewed
Accepted version
Permanent lenke
http://hdl.handle.net/11250/2627776Utgivelsesdato
2019Metadata
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Originalversjon
Belhadi, H., Akli-Astouati, K., Djenouri, Y., Lin, J. C.-W., & Wu, J. M.-T. (2019). GFSOM: Genetic feature selection for ontology matching. In J.-S. Pan, J. C.-W. Lin, B. Sui, & S.-P. Tseng (Eds.), Genetic and Evolutionary Computing. Genetic and evolutionary computing: Proceedings of the Twelfth International Conference on Genetic and Evolutionary Computing, December 14-17, Changzhou, Jiangsu, China (pp. 655-660). 10.1007/978-981-13-5841-8_68Sammendrag
This paper studies the ontology matching problem and proposes a genetic feature selection approach for ontology matching (GFSOM), which exploits the feature selection using the genetic approach to select the most appropriate properties for the matching process. Three strategies are further proposed to improve the performance of the designed approach. The genetic algorithm is first performed to select the most relevant properties, and the matching process is then applied to the selected properties instead of exploring all properties of the given ontology. To demonstrate the usefulness and accuracy of the GFSOM framework, several experiments on DBpedia ontology database are conducted. The results show that the ontology matching process benefits from the feature selection and the genetic algorithm, where GFSOM outperforms the state-of-the-art ontology matching approaches in terms of both the execution time and quality of the matching process.
Beskrivelse
This is a post-peer-review, pre-copyedit version of an article published in Advances in Intelligent Systems and Computing . The final authenticated version is available online at: https://doi.org/10.1007/978-981-13-5841-8_68