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Semantic distance between vague concepts in a framework of modeling with words

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Abstract

Effectively measuring the similarity or dissimilarity of two vague concepts plays a key step in reasoning and computing with vague concepts. In this paper, we define semantic distances between data instances and vague concepts based on modeling vagueness in a framework called label semantics. We also propose two clustering methods based on these sematic distances, which can cluster data instances and vague concepts simultaneously. To evaluate our approach, we conduct several experimental studies on three datasets including Corel images and labels, Reuters-21578, and TDT2. It is illustrated that the proposed distances have the ability to effectively evaluate sematic similarities between data instances and vague concepts.

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Notes

  1. Actually the third equation of this theorem doesn’t need the selection function assumption. It is a general property of appropriateness measure.

  2. Reuters-21578 is available at http://www.daviddlewis.com/resources/testcollections/reuters21578/.

  3. TDT2 is available at http://www.itl.nist.gov/iad/mig/publications/proceedings/darpa99/html/tdt110/tdt110.htm.

  4. LibSVM is available at http://www.csie.ntu.edu.tw/cjlin/libsvm.

  5. http://www.lextek.com/manuals/onix/stopwords1.html.

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Acknowledgements

This work is supported by the Natural Science Foundation of China (Grant Nos. 61572162 and 61272188) and the Zhejiang Provincial Key Science and Technology Project Foundation (No. 2017C01010).

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Weifeng Zhang, Hua Hu, Haiyang Hu & Jinglong Fang

  2. Science and Technology on Communication Information Security Control Laboratory, Jiangnan Electronic Communication Institute, Jiaxing, China

    Weifeng Zhang

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  1. Weifeng Zhang
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  2. Hua Hu
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Correspondence to Hua Hu or Haiyang Hu.

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Communicated by V. Loia.

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Zhang, W., Hu, H., Hu, H. et al. Semantic distance between vague concepts in a framework of modeling with words. Soft Comput 23, 3347–3364 (2019). https://doi.org/10.1007/s00500-017-2992-x

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