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Author: search.filters.author.Liu, Duen-RenSubject: search.filters.subject.Knowledge flow

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Now showing 1 - 5 of 5
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    RECOMMENDING DOCUMENTS VIA KNOWLEDGE FLOW-BASED GROUP RECOMMENDATION
    (2011)
    Lai, Chin-Hui
    ;
    Liu, Duen-Ren
    ;
    Chen, Ya-Ting
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    Recommender systems can mitigate the information overload problem and help workers retrieve knowledge based on their preferences. In a knowledge-intensive environment, knowledge workers need to access task related codified knowledge (documents) to perform tasks. A worker\'s document referencing behaviour can be modelled as a knowledge flow (KF) to represent the evolution of his/her information needs over time. Document recommendation methods can proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. However, most traditional recommendation methods do not consider workers\' knowledge flows and the information needs of the majority of a group of workers with similar knowledge flows. A group\'s needs may partially reflect the needs of an individual worker that cannot be inferred from his/her past referencing behaviour. Thus, we leverage the group perspective to complement the personal perspective by using a hybrid approach, which combines the KF-based group recommendation method (KFGR) with the user-based collaborative filtering method (UCF). The proposed hybrid method achieves a trade-off between the group-based and the personalized method by integrating the merits of both methods. Our experiment results show that the proposed method can enhance the quality of recommendations made by traditional methods.
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    Mining group-based knowledge flows for sharing task knowledge
    (2011-01-01)
    Liu, Duen-Ren
    ;
    Lai, Chin-Hui
    ;
    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 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. (C) 2010 Elsevier B.V. All rights reserved.
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    Discovering role-based virtual knowledge flows for organizational knowledge support
    (2013-04-01)
    Liu, Duen-Ren
    ;
    Lin, Chih-Wei
    ;
    Chen, Hui-Fang
    ;
    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 knowledge-intensive work environments, workers need task-relevant knowledge and documents to support the execution of tasks. A knowledge flow (KF) represents an individual's or group's knowledge-needs and referencing behavior of codified knowledge during the performance of organizational tasks. Through knowledge flows, organizations can provide workers with task-relevant knowledge to satisfy their knowledge-needs. In teamwork environments, knowledge workers with different roles and task functions usually have diverse knowledge-needs, but conventional KF models cannot satisfy such needs. In a previous work, we proposed a novel concept and theoretical model called Knowledge Flow View (KFV). Based on workers diverse knowledge-needs, the KFV model abstracts knowledge nodes of partial KFs and generates virtual knowledge nodes through a knowledge concept generalization procedure. However, the KFV model did not consider the diverse knowledge-needs of workers who play different roles in a team. Therefore, in this work, we propose a role-based KFV model that discovers role-based virtual knowledge flows to satisfy the knowledge-needs of different roles. First, we analyze the level of knowledge required by workers to fulfill various roles. Then, we develop role-based knowledge flow abstraction methods that generate appropriate virtual knowledge nodes to provide sufficient knowledge for each role. The proposed role-based ITV model enhances the efficiency of KF usage, as well as the effectiveness of knowledge sharing and knowledge support in organizations. (c) 2013 Elsevier B.V. All rights reserved.
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    Modeling the knowledge-flow view for collaborative knowledge support
    (2012-07-01)
    Liu, Duen-Ren
    ;
    Lin, Chih-Wei
    ;
    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 knowledge-based organizations, workers need task-relevant knowledge and documents to support their task performance. A knowledge flow (KF) represents the flow of an individual's or group members' knowledge-needs and the referencing sequence of documents in the performance of tasks. Through knowledge flows, organizations can provide task-relevant knowledge to workers to fulfill their knowledge-needs. Nevertheless, in a collaborative environment, workers usually have different knowledge-needs in accordance with their individual task functions. Conventional KF models do not provide workers with the different views of a knowledge flow that they require to meet these knowledge-needs. Several researchers have investigated KF models but they did not address the concept of the knowledge-flow view (KFV). This study proposes a theoretical model of the KFV using innovative methods. Basically, a KFV is a virtual knowledge flow derived from a base knowledge flow that abstracts knowledge concepts for individual workers based on their knowledge-needs. The KFV model in this study builds knowledge-flow views by abstracting knowledge nodes in a base knowledge flow to generate corresponding virtual knowledge nodes through an order-preserving approach and a knowledge concept generalization mechanism. The knowledge-flow views not only fulfill workers' different knowledge-needs but also facilitate knowledge support in teamwork. (C) 2012 Elsevier B.V. All rights reserved.
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    Integrating knowledge flow mining and collaborative filtering to support document recommendation
    (2009-12-01)
    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 a critical resource that organizations use to gain and maintain competitive advantages. In the constantly changing business environment, organizations Must exploit effective and efficient methods of preserving, sharing and reusing knowledge in order to help knowledge workers find task-relevant information. Hence, an important issue is how to discover and model the knowledge flow (KF) of workers from their historical work records. The objectives of a knowledge flow model are to understand knowledge workers' task-needs and the ways they reference documents, and then provide adaptive knowledge support. This work proposes hybrid recommendation methods based on the knowledge flow model, which integrates KF mining, sequential rule mining and collaborative filtering techniques to recommend codified knowledge. These KF-based recommendation methods involve two phases: a KF mining phase and a KF-based recommendation phase. The KF mining phase identifies each worker's knowledge flow by analyzing his/her knowledge referencing behavior (information needs), while the KF-based recommendation phase utilizes the proposed hybrid methods to proactively provide relevant codified knowledge for the worker. Therefore, the proposed methods use workers' preferences for codified knowledge as well as their knowledge referencing behavior to predict their topics of interest and recommend task-related knowledge. Using data collected from a research institute laboratory, experiments are conducted to evaluate the performance of the proposed hybrid methods and compare them with the traditional CF method. The results of experiments demonstrate that utilizing the document preferences and knowledge referencing behavior of workers can effectively improve the quality of recommendations and facilitate efficient knowledge sharing. (C) 2009 Elsevier Inc. All rights reserved.