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An Enhanced ELECTRE II Method for Multi-Attribute Ontology Ranking with Z-Numbers and Probabilistic Linguistic Term Set

Author

Listed:
  • Ameeth Sooklall

    (School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3201, South Africa)

  • Jean Vincent Fonou-Dombeu

    (School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3201, South Africa)

Abstract

The high number of ontologies available on the web to date makes it increasingly difficult to select appropriate ontologies for reuse. Many studies have attempted to provide support for ontology selection and ranking; however, the existing studies provide support for ontology ranking from an objective perspective as opposed to a subjective perspective. They do not take into account the qualitative aspects of ontologies. Furthermore, the existing methods have a limited focus on group environments. In this paper, a multi-criteria decision-making approach is presented for ontology ranking with the development of an enhanced model combining the ELECTRE II model with the Z-Probabilistic Linguistic Term Set (ZPLTS). The ZPLTS-ELECTRE II model enables decision-makers to model ontology ranking problems using both numerical and linguistic data. Furthermore, the newly proposed model provides support for ontology ranking in group settings, with an emphasis on modeling the differing levels of credibility of decision-makers using the ZPLTS, which allows decision-makers to not only specify their opinion but also specify their level of credibility. The model was applied to rank a set of mental health ontologies obtained from the BioPortal repository. The results showed that the method was able to rank the ontologies successfully. The results were further compared with the traditional ELECTRE II and the PLTS ELECTRE II methods, displaying superior modeling capabilities. This paper demonstrated the effectiveness of the newly proposed ZPLTS-ELECTRE II model for ontology ranking in a real-world context, but the method is not constrained to the ontology ranking domain; rather, it may be applied to other real-world decision problems as well.

Suggested Citation

  • Ameeth Sooklall & Jean Vincent Fonou-Dombeu, 2022. "An Enhanced ELECTRE II Method for Multi-Attribute Ontology Ranking with Z-Numbers and Probabilistic Linguistic Term Set," Future Internet, MDPI, vol. 14(10), pages 1-36, September.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:10:p:271-:d:920763
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    References listed on IDEAS

    as
    1. Tang, Xuli & Li, Xin & Ding, Ying & Song, Min & Bu, Yi, 2020. "The pace of artificial intelligence innovations: Speed, talent, and trial-and-error," Journal of Informetrics, Elsevier, vol. 14(4).
    2. Govindan, Kannan & Jepsen, Martin Brandt, 2016. "ELECTRE: A comprehensive literature review on methodologies and applications," European Journal of Operational Research, Elsevier, vol. 250(1), pages 1-29.
    3. Jiahui Chai & Sidong Xian & Sichong Lu, 2021. "Z probabilistic linguistic term sets and its application in multi-attribute group decision making," Fuzzy Optimization and Decision Making, Springer, vol. 20(4), pages 529-566, December.
    4. Ling Pan & Peijia Ren & Zeshui Xu, 2018. "Therapeutic Schedule Evaluation for Brain-Metastasized Non-Small Cell Lung Cancer with A Probabilistic Linguistic ELECTRE II Method," IJERPH, MDPI, vol. 15(9), pages 1-23, August.
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