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Knowledge structures in a knowledge base
Author(s) -
Li Zhaowen,
Li Qingguo,
Zhang Rongrong,
Xie Ningxin
Publication year - 2016
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12183
Subject(s) - knowledge base , computer science , rough set , knowledge extraction , knowledge based systems , open knowledge base connectivity , set (abstract data type) , knowledge management , base (topology) , domain knowledge , artificial intelligence , data mining , theoretical computer science , personal knowledge management , organizational learning , mathematics , programming language , mathematical analysis
Rough set theory is a useful tool for dealing with imprecise knowledge. One of the advantages of rough set theory is the fact that an unknown target concept can be approximately characterized by existing knowledge structures in a knowledge base. This paper explores knowledge structures in a knowledge base. Knowledge structures in a knowledge base are firstly described by means of set vectors and relationships between knowledge structures divided into four classes. Then, properties of knowledge structures are discussed. Finally, group, lattice, mapping, and soft characterizations of knowledge structures are given.