Measures of uncertainty for knowledge bases

被引：0
作者：
Zhaowen Li
Gangqiang Zhang
Wei-Zhi Wu
Ningxin Xie
机构：
[1] Yulin Normal University,Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education
[2] Guangxi University for Nationalities,School of Software and Information Security
[3] Zhejiang Ocean University,School of Mathematics, Physics and Information Science
[4] Zhejiang Ocean University,Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province
来源：
Knowledge and Information Systems | 2020年 / 62卷
关键词：
Rough set theory; Granular computing; Knowledge base; Knowledge granule; Knowledge structure; Dependence; Uncertainty; Measure; Effectiveness;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：611 / 637
页数：26
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