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Fuzzy Set Similarity Between Fuzzy Words

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Fuzzy Techniques: Theory and Applications (IFSA/NAFIPS 2019 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1000))

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Abstract

Fuzzy set similarity measures determine the similarity between two fuzzy sets. Semantic similarity measures determine the similarity between concepts within an ontology. Research in determining sentence similarity has used semantic similarity between fuzzy words that have been structured in an ontology based on data provided by human experts to create fuzzy sets for these fuzzy words. The research uses four different kinds of fuzzy set similarity measures, three that are standard ones for type-1 fuzzy sets and another one based on the distance between defuzzified and normalized COGs for type-2 fuzzy sets and examines both the Pearson and Spearman correlations among these different fuzzy set similarity measures on fuzzy words from four of the six categories established in past sentence similarity research. The results show that all standard fuzzy set measures are highly correlated though some categories of fuzzy words result in higher correlations than others. The standard fuzzy set similarity measures have lower correlation with the one developed for similarity between type-2 fuzzy sets using their defuzzified and normalized COGs.

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Acknowledgment

This continued research is based on the data provided for the previous research in [9] by researchers Keeley Crockett and Naeemeh Adel. We want to thank them for their assistance in making this research possible.

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Authors and Affiliations

  1. Computer Science and Software Engineering, Miami University, Oxford, OH, 45056, USA

    Valerie Cross & Valeria Mokrenko

Authors
  1. Valerie Cross
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  2. Valeria Mokrenko
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Corresponding author

Correspondence to Valerie Cross .

Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, USA

    Ralph Baker Kearfott

  2. Center for Computing Research (CIC), Instituto Politecnico Nacional, Ciudad de Mexico, Mexico

    Ildar Batyrshin

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Marek Reformat

  4. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Martine Ceberio

  5. Department of Computer Science, University of Texas at El Paso , El Paso, TX, USA

    Vladik Kreinovich

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Cross, V., Mokrenko, V. (2019). Fuzzy Set Similarity Between Fuzzy Words. In: Kearfott, R., Batyrshin, I., Reformat, M., Ceberio, M., Kreinovich, V. (eds) Fuzzy Techniques: Theory and Applications. IFSA/NAFIPS 2019 2019. Advances in Intelligent Systems and Computing, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-21920-8_20

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