摘要
知识管理自学习案例(KML-Case)体现"干中学"思想,对破解知识管理系统(KMS)的知识获取瓶颈、实现系统自学习与自组织意义重要。KML-Case适配效益决定KML-Case应用效果;然而,业内大多数系统模型与实践系统为空适配,导致案例匹配失败后的系统不作为,严重束缚了KML-Case应用价值。鉴于此,首先阐释了KML-Case适配内涵,在深入分析业内相关成果的基础上,提出了本研究的出发点,并建立KML-Case多案例综合诱导型适配机制以提升KML-Case应用效益。对此,深入讨论了KML-Case适配源案例的匹配方法以及适配案例集的确定策略;分析了基于KML-Case适配案例集构建知识表达系统的技术方案;通过案例条件方面精化以及冗余初等范畴削减两步机制,详细设计了KML-Case适配解轨迹求解算法;最后,阐明了基于解轨迹的KML-Case多案例综合诱导型适配方法与策略。算例表明,本文方法有效避免了系统匹配失败后的不作为、提升了KMS的问题求解能力。
Knowledge management self-learning case （KML-Case）, reflecting the idea of ＂learning by doing＂, is significant in relieving the bottleneck of knowledge acquisition in knowledge management system （KMS） and achieving self-learning and self-organization. It is well known that the performance of KML- Case depends on KML-Case adaptation. However, there exists no adaptation in most theoretical models and practical systems, which leads to a system not working when case matching fails, and the application value of KML-Case thus is severely limited. To deal with this problem, we first discuss the connotation of KML-Case adaptation based on the literature, mechanism. We then study the matching method of and establish KML-Case ada a multi-case derivational adaptation ptation of source cases in depth and the strategies of determining the adapted case set. We analyze the technical solution to build knowledge representation system based on the KML-Case adaptation case set, and develop the algorithm for KML- Case adaptation solution trajectory, including refinement of case conditions and reduction of redundant primary categories. Finally, we propose the multi-case derivational adaptation methods and strategies based on the KML-Case solution trajectory. We use an example to show that the proposed mechanism iseffective to avoid system omission after case matching failure thus enhances the problem-solving capability of KMS.
出处
《系统管理学报》
CSSCI
2014年第6期845-853,共9页
Journal of Systems & Management
基金
国家社会科学基金资助项目(11CTQ023)
关键词
知识管理
自学习案例
案例适配
解轨迹
knowledge management
KML-case
case adaptation
solution trajectory
