dc.contributor.author | Belhadi, Hiba | |
dc.contributor.author | Akli-Astouati, Karima | |
dc.contributor.author | Djenouri, Youcef | |
dc.contributor.author | Lin, Chun Wei | |
dc.contributor.author | Wu, Jimmy Ming-Tai | |
dc.date.accessioned | 2019-11-11T14:12:45Z | |
dc.date.available | 2019-11-11T14:12:45Z | |
dc.date.created | 2019-07-02T14:05:10Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | 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). | nb_NO |
dc.identifier.issn | 2194-5357 | |
dc.identifier.uri | http://hdl.handle.net/11250/2627776 | |
dc.description | 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 | nb_NO |
dc.description.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. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Springer | nb_NO |
dc.subject | semantic web | nb_NO |
dc.subject | ontology matching | nb_NO |
dc.subject | feature selection | nb_NO |
dc.subject | genetic algorithm | nb_NO |
dc.title | GFSOM: Genetic Feature Selection for Ontology Matching | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.subject.nsi | VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420 | nb_NO |
dc.source.pagenumber | 655-660 | nb_NO |
dc.source.volume | 834 | nb_NO |
dc.source.journal | Advances in Intelligent Systems and Computing | nb_NO |
dc.identifier.doi | 10.1007/978-981-13-5841-8_68 | |
dc.identifier.cristin | 1709442 | |
cristin.unitcode | 203,12,4,0 | |
cristin.unitname | Institutt for data- og realfag | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |