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Measures of uncertainty for knowledge bases

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Abstract

This paper investigates measures of uncertainty for knowledge bases by using their knowledge structures. Knowledge structures of knowledge bases are first introduced. Then, dependence and independence between knowledge structures of knowledge bases are proposed, which are characterized by inclusion degree. Next, measures of uncertainty for a given knowledge base are studied, and it is proved that the proposed measures are based on the knowledge structure of this knowledge base. Finally, a numerical experiment is conducted to show performance of the proposed measures and effectiveness analysis is done from two aspects of dispersion and correlation in statistics. These results will be significant for understanding the essence of uncertainty for knowledge bases.

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Acknowledgements

The authors would like to thank the editors and the anonymous reviewers for their valuable comments and suggestions which have helped immensely in improving the quality of the paper. This work is supported by This work is supported by National Natural Science Foundation of China (Nos. 41631179 and 61573321), Natural Science Foundation of Guangxi (Nos. 2018GXNSFDA294003, 2018GXNSFDA281028 and 2018GXNSFAA294134), Zhejiang Provincial Natural Science Foundation of China (Nos. LY18F030017), High Level Innovation Team Program from Guangxi Higher Education Institutions of China (Document No. [2018] 35), Key Laboratory of Software Engineering in Guangxi University for Nationalities (No. 2018-18XJSY-03) and Engineering Project of Undergraduate Teaching Reform of Higher Education in Guangxi (No. 2017JGA179).

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Authors and Affiliations

  1. Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University, Yulin, 537000, Guangxi, People’s Republic of China

    Zhaowen Li

  2. School of Software and Information Security, Guangxi University for Nationalities, Nanning, 530006, Guangxi, People’s Republic of China

    Gangqiang Zhang & Ningxin Xie

  3. School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan, 316022, Zhejiang, People’s Republic of China

    Wei-Zhi Wu

  4. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan, 316022, Zhejiang, People’s Republic of China

    Wei-Zhi Wu

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  1. Zhaowen Li
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  2. Gangqiang Zhang
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  3. Wei-Zhi Wu
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  4. Ningxin Xie
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Correspondence to Zhaowen Li or Gangqiang Zhang.

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Li, Z., Zhang, G., Wu, WZ. et al. Measures of uncertainty for knowledge bases. Knowl Inf Syst 62, 611–637 (2020). https://doi.org/10.1007/s10115-019-01363-0

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