Rough approximations based on soft binary relations and knowledge bases

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• 作者：Zhaowen Li ; Ningxin Xie ; Ninghua Gao
• 关键词：Rough approximation ; Rough set ; Soft binary relation ; Topology ; Knowledge base ; Knowledge structure
• 刊名：Soft Computing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：21
• 期：4
• 页码：839-852
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics;
• 出版者：Springer Berlin Heidelberg
• ISSN：1433-7479
• 卷排序：21

文摘

This paper studies rough approximations based on soft binary relations. Firstly, two pairs of rough approximations based on soft binary relations are investigated, and their properties are determined. Secondly, rough sets with respect to some parameter and the set of parameters are introduced, and the fact that every rough set is a special case of E-rough sets where E is the set of parameters is demonstrated. Thirdly, rough soft sets induced by soft binary relations are proposed, and their lattice structures are given. Fourthly, two kinds of topologies induced by soft reflexive relations are investigated. Finally, the fact that there exists a one-to-one correspondence between the family of all knowledge bases and the family of all soft equivalence relations is proved, and soft characterizations of knowledge structures in knowledge bases are provided, which shows that we can study knowledge bases using soft set theory.