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Composable Conditions for Constructing Knowledge Structure Based on Variable Precision FT-Rough Set Model

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  • Jingjing Yang
  • Chuanyi Huang
  • Jinjin Li

Abstract

Constructing a knowledge structure using the variable precision FT-rough set model is an effective approach. Because directly constructing a knowledge structure for a subject or field is challenging, synthesizing global information from local information is a viable solution. However, local information often overlaps (partially); therefore, ensuring consistency between global and local information is crucial, which is an urgent issue to address. Therefore, based on the variable precision FT-rough set model and the knowledge structure constructed from it, this paper proposes and proves the conditions for the composability of knowledge structures constructed using the lower (upper) inverse operator of the variable precision FT-rough set. Under these conditions, the knowledge structures constructed, respectively, from the local fuzzy approximation spaces can be integrated into the global knowledge structure.

Suggested Citation

  • Jingjing Yang & Chuanyi Huang & Jinjin Li, 2026. "Composable Conditions for Constructing Knowledge Structure Based on Variable Precision FT-Rough Set Model," Journal of Mathematics, Hindawi, vol. 2026, pages 1-19, February.
    • Handle: RePEc:hin:jjmath:4871745
    DOI: 10.1155/jom/4871745
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