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GFSOM: Genetic Feature Selection for Ontology Matching

Belhadi, Hiba; Akli-Astouati, Karima; Djenouri, Youcef; Lin, Chun Wei; Wu, Jimmy Ming-Tai
Journal article, Peer reviewed
Accepted version
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URI
http://hdl.handle.net/11250/2626341
Date
2019
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  • Institutt for datateknologi og informatikk [4881]
  • Publikasjoner fra CRIStin - NTNU [26591]
Original version
Advances in Intelligent Systems and Computing. 2019, 834 655-660.   10.1007/978-981-13-5841-8_68
Abstract
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.
Publisher
Springer Verlag
Journal
Advances in Intelligent Systems and Computing

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