Mining group-based knowledge flows for sharing task knowledge

Duen-Ren Liu*, Chin Hui Lai

*Corresponding author for this work

Research output: Contribution to journalArticle

28 Scopus citations

Abstract

In an organization, knowledge is the most important resource in the creation of core competitive advantages. It is circulated and accumulated by knowledge flows (KFs) in the organization to support workers' task needs. Because workers accumulate knowledge of different domains, they may cooperate and participate in several task-based groups to satisfy their needs. In this paper, we propose algorithms that integrate information retrieval and data mining techniques to mine and construct group-based KFs (GKFs) for task-based groups. A GKF is expressed as a directed knowledge graph which represents the knowledge referencing behavior, or knowledge flow, of a group of workers with similar task needs. Task-related knowledge topics and their relationships (flows) can be identified from the knowledge graph so as to fulfill workers' task needs and promote knowledge sharing for collaboration of group members. Moreover, the frequent knowledge referencing path can be identified from the knowledge graph to indicate the frequent knowledge flow of the workers. To demonstrate the efficacy of the proposed methods, we implement a prototype of the GKF mining system. Our GKF mining methods can enhance organizational learning and facilitate knowledge management, sharing, and reuse in an environment where collaboration and teamwork are essential.

Original languageEnglish
Pages (from-to)370-386
Number of pages17
JournalDecision Support Systems
Volume50
Issue number2
DOIs
StatePublished - 1 Jan 2011

Keywords

  • Data mining
  • Group-based knowledge flow
  • Knowledge flow
  • Knowledge graph
  • Knowledge sharing
  • Task
  • Topic

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