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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Liu, Duen-RenSubject: search.filters.subject.knowledge management

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Now showing 1 - 4 of 4
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    Discovering Workflow-Aware Virtual Knowledge Flows for Knowledge Dissemination
    (2013-01-01)
    Shen, Minxin
    ;
    Liu, Duen-Ren
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    In order to effectively disseminate task-relevant and process-scope knowledge, knowledge-intensive enterprises adopt knowledge flows to explicitly represent workers\' knowledge needs and referencing behavior of codified knowledge during the execution of business tasks. However, due to differences in expertise and experience, individual workers impose varied knowledge needs on the knowledge flows directed by the workflows they participate in. This study proposes a model of workflow-aware knowledge-flow views, i.e. virtual knowledge flows abstracted from workflow-driven knowledge flows, to provide adaptable knowledge granularity. Moreover, a text mining approach is developed to derive knowledge-flow views from codified knowledge objects of knowledge flows, such as documents. Both task knowledge semantics and task execution sequences are utilized to evaluate the degrees of workers\' knowledge demands in workflow contexts. Knowledge management systems can thus present different abstracted knowledge flows to diverse workflow participants, and facilitate knowledge sharing and collaboration.
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    Collaborative relevance assessment for task-based knowledge support
    (2008-01-01)
    Liu, Duen-Ren
    ;
    Wu, I-Chin
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    The operations and management activities of enterprises are mainly task-based and knowledge intensive. Accordingly, an important issue in deploying knowledge management systems is the provision of task-relevant information (codified knowledge) to meet the information needs of knowledge workers during the execution of a task. Codified knowledge extracted from previously executed tasks can provide valuable knowledge about conducting the task-at-hand (current task), and is a valuable information source for constructing a task profile that models a worker's task needs, i.e., information needs for the current task. In this paper, we propose a novel task-relevance assessment approach that evaluates the relevance of previous tasks in order to construct a task profile for the current task. The approach helps knowledge workers assess the relevance of previous tasks through linguistic evaluation and the collaboration of knowledge workers. In addition, applying relevance assessment to a large number of tasks may create an excessive burden for workers. Thus, we propose a novel two-phase relevance assessment method to help workers conduct relevance assessment effectively. Furthermore, a modified relevance feedback technique, which is integrated with the task-relevance assessment method, is employed to derive the task profile for the task-at-hand. Consequently, task-based knowledge support can be enabled to provide knowledge workers with task-relevant information based on task profiles. Empirical experiments demonstrate that the proposed approach models workers' task-needs effectively and helps provide task-relevant knowledge. (C) 2007 Elsevier B.V. All rights reserved.
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    Document recommendation for knowledge sharing in personal folder environments
    (2008-08-01)
    Liu, Duen-Ren
    ;
    Lai, Chin-Hui
    ;
    Huang, Chiu-Wen
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    Sharing sustainable and valuable knowledge among knowledge workers is a fundamental aspect of knowledge management. In organizations, knowledge workers usually have personal folders in which they organize and store needed codified knowledge (textual documents) in categories. In such personal folder environments, providing knowledge workers with needed knowledge from other workers' folders is important because it increases the workers' productivity and the possibility of reusing and sharing knowledge. Conventional recommendation methods can be used to recommend relevant documents to workers; however, those methods recommend knowledge items without considering whether the items are assigned to the appropriate category in the target user's personal folders. In this paper, we propose novel document recommendation methods, including content-based filtering and categorization, collaborative filtering and categorization, and hybrid methods, which integrate text categorization techniques, to recommend documents to target worker's personalized categories. Our experiment results Show that the hybrid methods Outperform the pure content-based and the collaborative filtering and categorization methods. The proposed methods not only proactively notify knowledge workers about relevant documents held by their peers, but also facilitate push-mode knowledge sharing. (C) 2007 Elsevier Inc. All rights reserved.
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    Mining knowledge flow for modeling the information needs of task-based groups
    (2008)
    Lai, Chin-Hui
    ;
    Liu, Duen-Ren
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    Knowledge is the most important resource to create core competitive advantages for an organization. Such knowledge is circulated and accumulated by a knowledge flow (KF) in an organization to support worker tasks. Workers may cooperate and participate in several task-based groups to fulfill their needs. In this paper, we propose a group-based knowledge flow mining algorithm which integrates information retrieval and data mining techniques for mining and constructing the group-based KF (GKF) for task-based groups. The GKF is expressed as a directed knowledge graph to represent the knowledge referencing behavior for a group of workers with similar task needs. The frequent knowledge referencing paths are identified from the knowledge graph to indicate the frequent knowledge flows of the workers. We also implement a prototype of GKF mining system to demonstrate the effectiveness of our proposed method.