The Optimal Inference Approximate Algorithm in Weighted Hypergraph based on Granular Computing

被引：0

作者：

Li, Wei ^{[1
,2
]}

Wang, Rujing ^{[1
]}

Jia, Xiufang ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Intelligent Machines, Hefei, Anhui, Peoples R China

[2] Univ Sci & Technol China, Dept Automat, Hefei, Anhui, Peoples R China

来源：

PROCEEDINGS OF 2016 5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT) | 2016年

关键词：

Granular Computing; Weighted Hypergraph; Uncertainty Inference; Quotient Space Theory;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The relation of knowledge is vividly represented by hypergraph in big knowledge base. In this paper, hierarchical quotient space theory in granular computing model is applied to weighted hypergraph, so uncertainty inference of knowledge in big knowledge base is converted to the optimal path approximate of weighted hypergraph based on granular computing model. Thus, the complex degree of solving problem is simplified and the best inference path is obtained.

引用

页码：273 / 276

页数：4

共 13 条

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