摘要
在知识经济背景下,取代劳动力和资本,知识上升为核心生产资料、价值创造的主体来源;知识管理成为企业培育核心竞争力、实现可持续发展的基本途径。知识管理系统自学习案例是支撑知识管理系统自组织机制、确保其运行效率与有效性的知识基础,匹配策略与算法是案例获取、应用与进化的支撑机制,两者共同影响知识管理系统的实施效益。鉴于此,首先在对业内相关文献进行综述研究的基础上,针对知识管理系统自学习案例方面值的非精确特征,分析已有研究之不足,阐明了本研究的出发点,讨论了对案例方面值实施归一化与离散化的实现方法;其次,基于案例数据,采用过程严谨、客观定量的粗集赋权法,实现对案例视图向量的确定;而后,深入研究知识管理系统自学习案例非精确方面距离的计算方法,通过灰关联分析方法计算得到知识管理系统自学习案例与待解问题间的方面相似度,并设计了案例标识与相似度匹配序列确定算法,从而完成含非精确方面的知识管理系统自学习案例的匹配计算。文末算例分析表明,本文提出的匹配模型突破了已有方法的条件束缚、弥补了现有研究之不足,收到了良好效果。
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 uo for their shortage.
出处
《情报杂志》
CSSCI
北大核心
2015年第10期134-139,164,共7页
Journal of Intelligence
基金
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
