Study and Implement of the Knowledge Representation of Fuzzy Conceptual Graph

Chinese

您当前的位置：

首页 >

文章列表页 >

Study and Implement of the Knowledge Representation of Fuzzy Conceptual Graph

更新时间：2026-09-04

- Study and Implement of the Knowledge Representation of Fuzzy Conceptual Graph
- Vol. 27, Issue 11, Pages: 25-29(2010)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
移动端阅览
模糊概念图知识表示方法的研究与实现[J]. 微电子学与计算机, 2010, 27(11): 25-29.

Study and Implement of the Knowledge Representation of Fuzzy Conceptual Graph[J]. 2010, 27(11): 25-29.
模糊概念图知识表示方法的研究与实现[J]. 微电子学与计算机, 2010, 27(11): 25-29. DOI：

Study and Implement of the Knowledge Representation of Fuzzy Conceptual Graph[J]. 2010, 27(11): 25-29. DOI：

摘要

模糊概念图作为一种模糊不确定知识表示方法

已经引起人工智能学者的广泛关注.在MORTON提出的模糊概念图表示方法中

用模糊度表示概念的不确定性具有一定的局限性.为了克服这种局限性

将模糊数学理论同传统模糊概念图相结合

从模糊不确定知识表示方面对概念图进行研究

采用模糊集表示模糊概念图中概念节点和关系节点的模糊度

提出一种改进的模糊概念图知识表示方法

设计了相应的存储结构

并研究了模糊概念图的匹配操作. 改进的模糊概念图已经应用于主观题自动阅卷方法中

正确率较高.

Abstract

As a knowledge representation of fuzzy uncertainty

fuzzy conceptual graphs have attracted wide attention from scholars of artificial intelligence. In MORTONs representation of fuzzy conceptual graphs

fussiness has certain limitation. To overcome this limitation

we combine fuzzy mathematics theory with traditional fuzzy conceptual graph and study fuzzy conceptual graph interms of fuzzy uncertain knowledge representation. An improved representation of fuzzy conceptual graph is presented in this paper. Fuzzy set is used to represent the fuzziness of concepts and conceptual relations in fuzzy conceptual graph. The storage structure of fuzzy conceptual graph is designed according to this representation

and the matching operation rules of fuzzy conceptual graph are established. Improved fuzzy conceptual graph has been applied to methods of automatic checking over subjective examination

the accuracy is high.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on the design of blockchain-based AI-generated content traceability framework

Aerial small object detection algorithm based on improved RT-DETR

High gain stability high temperature operational amplifier based on reverse body bias

Implementation and verification of a dual-chain power gating strategy for low power design

Monte carlo analysis of V-layout trench SiC neutron detector

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰