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Optimizing Ontology Alignments by Using Neural NSGA-II

Author

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  • Mohamed Biniz

    (Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal, Morocco)

  • Rachid El Ayachi

    (Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal, Morocco)

Abstract

In this article, the authors propose a new hybrid approach based on a continuous Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a neural network to refine the alignment results. This approach consists of three phases: (i) pre-alignment phase which allows to identify the formats of input ontologies, to adapt them and to transform them into Ontology Web Language (OWL) in order to solve the problem of heterogeneity of representation. (ii) alignment phase which combines syntactic and linguistic matching techniques and methods, based on the relevant attributes per different points of syntactic and structural technic. (iii) The post-alignment phase which optimizes the matching by a hybrid technique of continuous NSGA-II and networks of neurons. This approach is compared with the greatest systems per the Ontology Alignment Evaluation Initiative (OAEI) standard. The experimental results appear that the proposed approach is effective.

Suggested Citation

  • Mohamed Biniz & Rachid El Ayachi, 2018. "Optimizing Ontology Alignments by Using Neural NSGA-II," Journal of Electronic Commerce in Organizations (JECO), IGI Global, vol. 16(1), pages 29-42, January.
    • Handle: RePEc:igg:jeco00:v:16:y:2018:i:1:p:29-42
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