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

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Science Research Management ›› 2015, Vol. 36 ›› Issue (8) : 77-85.

Wang Xiaojian^1,2, Liu Yanping^1,3

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Abstract

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 modern enterprises more comprehensively. This paper studies the methodologies of behavior modeling on web data and heterogeneous information fusion for enterprise 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 processing techniques as the pattern representation method. We build our knowledge analytical method based on the statistical machine 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 correlation 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.

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knowledge management / online behavior modeling / multi-source knowledge fusion / correlation model

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Wang Xiaojian, Liu Yanping. A novel enterprise knowledge management methods based on multiple source knowledge fusion[J]. Science Research Management. 2015, 36(8): 77-85

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