Paper Info
Title
Measures of uncertainty for knowledge bases
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.
Year
DOI
Venue
2020
10.1007/s10115-019-01363-0
Knowledge and Information Systems
Keywords
Field
DocType
Rough set theory, Granular computing, Knowledge base, Knowledge granule, Knowledge structure, Dependence, Uncertainty, Measure, Effectiveness
Data mining,Computer science,Knowledge structure,Rough set,Granular computing,Artificial intelligence,Knowledge base,Machine learning
Journal
Volume
Issue
ISSN
62
2
0219-1377
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Zhaowen Li113616.20
Gangqiang Zhang2468.63
Wei-Zhi Wu33205125.29
Ningxin Xie4698.08