Semantic distance between vague concepts in a framework of modeling with words
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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.
KeywordsVague concepts Label semantics Semantic distance Clustering
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).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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