Uncertain Distribution-Based Similarity Measure of Concepts

Shuai Li,Juanli Zeng,Zhipeng Qi,Jie Yang

doi:10.1155/2020/5074956

Abstract

The similarity of concepts is a basic task in the field of artificial intelligence, e.g., image retrieval, collaborative filtering, and public opinion guidance. As a powerful tool to express the uncertain concepts, similarity measure based on cloud model (SMCM) is always utilized to measure the similarity between two concepts. However, current studies on SMCM have two main limitations: (1) the similarity measures based on conceptual intension lack interpretability for merging the numerical characteristics and cannot discriminate some different concepts. (2) The similarity measures based on conceptual extension are always instable and inefficient. To address the above problems, an uncertain distribution-based similarity measure of cloud model (UDCM) is proposed in this paper. By analyzing the definition of the CM, we propose a new complete uncertainty including first-order and second-order uncertainty to calculate the uncertainty more accurately. Then, based on the difference between the complete uncertainty of two concepts, the computing process of UDCM and its some properties are introduced. Finally, we exhibit its advantages by comparing with other methods and verify its validity by experiments.

Highlights

Shooting (c) more close to A than C from the aspect of shooting level. at is, considering the similarity from different angles, different results can be obtained
To address the above problems, in this paper, we propose a new notion called complete uncertainty to depict the whole uncertainty in the process from numerical characteristics to the conceptual extension. en, a new similarity measure based on cloud model (SMCM) is presented based on completed uncertainty, which reflects the similarity of the uncertain distribution of two concepts
Likeness comparing method based on cloud model (LICM) [19] employs included angle cosine of vectors composed by numerical characteristics to measure similarity

Summary

Cloud Model

Gaussian cloud model introduces three numerical characteristics including Ex, En, and He, which denote mathematical expectation, entropy, and hyperentropy. Is method is called concept extension-based similarity measure (CS). It is understandable and accords with human cognition. Likeness comparing method based on cloud model (LICM) [19] employs included angle cosine of vectors composed by numerical characteristics to measure similarity. It has high efficiency, but ignores the relationships among numerical characteristics leading to unreasonable results sometimes. Ese methods employ characteristic curves to denote the certainty degrees of cloud drops and use similarity of uncertainty to measure similarity of concepts. We define a new notion of CM called complete (first-order and second-order) uncertainty and propose a new SMCM based on complete uncertainty, which focuses on the difference between distributions of two concepts

Uncertain Distribution-Based SMCM

Experimental Analysis

Method FDCM UDCM

Conclusion