摘要
知识作为企业核心竞争力的关键要素,是创造组织价值的源泉.知识体量的快速增长导致"知识迷向"问题日益突出,案例知识匹配可有效缓解这一问题、提升知识转移效益、改善知识应用效果.在前人研究的基础上,提出了基于空间压缩与WFT视图改进的案例知识匹配方法.方法首先基于GA-RS算法提取最优的条件属性集,实现匹配空间的纵向压缩,并兼顾条件属性与决策属性的相关度,借鉴WFT原理优化案例视图;而后,采用聚类算法横向压缩遍历空间;最后,基于属性权重利用模糊贴合度计算用户需求与案例知识的视图相似度,并确定匹配结果.算例分析表明,与已有案例知识匹配方法相比,算法具有可行性及进步性.
knowledge as a key element of the core competitiveness of an enterprise,is also the source of creating organizational value.With the rapid growth of knowledge volume,the problem of "knowledge fascination" has become increasingly prominent.Case knowledge matching,which can effectively alleviate this problem,enhance the efficiency of knowledge transfer and improve the effect of knowledge application.On the basis of previous research,a case knowledge matching method based on space compression and WFT view improvement is proposed.This method first extracts the optimal set of condition attributes based on the GA-RS algorithm to achieve vertical compression of the matching space,while taking into account the correlation between condition attributes and decision attributes,and optimizes the case view based on the WFT principle;Later,this paper uses a clustering algorithm to compress the traverse space horizontally;At last,calculates the view similarity between user needs and case knowledge based on the attribute weight and uses the fuzzy fit degree,and finally determines the matching result.The analysis of numerical examples shows that the algorithm in this paper has certain advantages and advancements compared with existing case knowledge matching methods.
作者
张建华
刘艺琳
李方方
温丹丹
ZHANG Jian-hua;LIU Yi-lin;LI Fang-fang;WEN Dan-dan(School of Management Engineering,Zhengzhou University,Zhengzhou 450001,China)
出处
《数学的实践与认识》
2021年第17期1-10,共10页
Mathematics in Practice and Theory
基金
国家社会科学基金项目“隐性知识深度服务体系研究”(19BTQ035)的研究成果之一。
