情报杂志Issue(10)：134-139,164,7.DOI:10.3969/j.issn.1002-1965.2015.10.023
知识管理系统含非精确方面自学习案例视图匹配研究
Research on Matching Method for Self-learning Cases of Knowledge Management System Including Imprecise Aspects
摘要
Abstract
In the background of knowledge-based economy, replacing labor and capital,knowledge becomes the core production and the main source of value creation. Knowledge management (KM) is the basic way to build organizational core competence and achieve its sustainable development. Knowledge Management System (KMS) self-learning case(KMSLC)is the knowledge base to support the self-organization mechanism of KMS and ensure its efficiency and effectiveness. Matching strategy and algorithm is the support mechanism to KMSLC acquisition, application and evolution, the overall KM implementation is affected by them. Therefore, in this paper, based on the literature review, oriented to grey aspects of KMSLC, the starting point of this research is explained. And then, the characteristics of KMSLC are analyzed, and the normalization and discretization for aspect value is discussed, firstly. Secondly, by the method of Rough Sets (RS), the weight of KMSLC aspect is calculated. Thirdly, the normalized problem in KMSLC matching process is analyzed deeply, and the algorithm to calculate the distance of grey aspects is also studied. Simultaneously, the aspect similarity between KMSLC and the problem to be solved is calculated through grey relational analysis (GRA), and then the matched KMSLC set is obtained based on it. The example shows that the proposed mechanism, with good results, breaks original methods' shackles and makes up for their shortage.关键词
知识管理系统/自学习案例/非精确方面/视图匹配/粗集/灰关联分析Key words
KMS/self-learning case/imprecise aspect/view matching/rough sets/grey relational analysis分类
管理科学引用本文复制引用
张建华..知识管理系统含非精确方面自学习案例视图匹配研究[J].情报杂志,2015,(10):134-139,164,7.基金项目
国家社会科学基金项目“知识管理 RS-CBR 自学习系统研究”(编号:11CTQ023)研究成果。 （编号:11CTQ023）
