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Fuzzy Set Similarity Between Fuzzy Words

  • Valerie CrossEmail author
  • Valeria Mokrenko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1000)

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.

Keywords

Fuzzy set similarity Sentence similarity Semantic similarity Computing with words (CWW) Geometric distance-based measures Partial matching measures Jaccard fuzzy set similarity measure 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science and Software EngineeringMiami UniversityOxfordUSA

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