基于多源知识融合的企业知识管理方法

A novel enterprise knowledge management methods based on multiple source knowledge fusion

作为知识经济时代出现的新兴管理方法,企业知识管理的一个重要的研究课题,是对组织成员和人力资源的知识管理。传统的知识管理方法基于封闭的企业管理历史数据,不能深入地挖掘企业管理知识。本文拟从网络数据的行为建模和多源数据融合的角度,探索新的企业知识管理方法。以企业知识管理为研究目标,以石化企业员工为研究对象,以自然语言分析和处理为模式表示方法,以统计机器学习为知识分析工具,重点研究如何通过网络行为建模和多源数据融合,从而更全方位地对企业员工的性格、日常行为建立一套行之有效的分析机制,实现在开放网络时代的企业知识管理和绩效管理。在石化企业内部的知识管理实验性尝试表明,所提知识管理方法,能够更深入地对企业员工的行为进行分析和理解,能够更有效地将员工行为和企业人力资源中的若干关键指标建立相关性模型,从而为企业的知识管理和决策人员提供强有力的技术支持。

As a novel management theory of the knowledge - based economy, the key research problem of enterprise knowledge management is how to conduct management on organization members, human resources and customers. Traditional knowledge management approaches are based on the closed enterprise management history data. However, they are not able to discover the knowledge for modem enterprises more comprehensively. This paper studies the methodologies of behavior modeling on web data and heterogeneous information fusion for emerprise knowledge management. We serve enterprise knowledge management as the research objective, treat the employees of the petrochemical enterprises as the research target, and use the natural language pro- cessing techniques as the pattern representation method. We build our knowledge analytical method based on the statistical ma- chine learning for heterogeneous information fusion mechanisms, in order to construct a comprehensive behavior understanding and knowledge discovery methodologies, and try to use big data and information fusion to construct the enterprise knowledge based management and performance management. Extensive knowledge management experiments in petrochemical enterprises validate the effectiveness of our approach, which is capable of analyzing and understanding the enterprise employees, and build the corre- lation model between the employee behavior and knowledge discovery objectives in enterprise human resource management. Therefore, our method provides strong technical support for enterprise knowledge management decision making.