以知識流探勘與文件推薦提供知識支援

Abstract

知識是獲得與維持組織競爭優勢的重要來源。在不斷變動的商業環境中，組織必須使用有效的方法來保留知識、分享知識和知識再利用，以協助知識工作者尋找工作相關的資訊。因此，要如何從工作者過去的工作記錄中，發掘與建構知識流（Knowledge Flow）是一個重要的議題。建立知識流模型的目的是在於，了解知識工作者的工作需求與參考知識的方式，進而提供適性化的知識支援。此外，組織中的知識是透過知識流的遞送與累積，而且知識工作者具備不同領域的知識，他們會參與以工作為基礎的群體，並進行合作，以滿足工作的需求。 本研究首先提出以知識流模型為基礎之混合式推薦方法，其整合知識流探勘、序列規則探勘，以及協同式過濾技術來推薦工作知識。這些以知識流為基礎的推薦方法包含二個階段：知識流探勘階段與知識流推薦階段。知識流探勘階段能藉由分析工作者的知識參考行為（資訊需求），以發掘工作者的知識流；而知識流推薦階段則利用所提出的混合式推薦方法，主動地提供相關知識給工作者。因此，根據工作者對於知識文件的喜好與知識參考行為，本研究方法能預測工作者感興趣的主題，進而推薦工作相關的知識文件給工作者。在實驗中，我們利用某研究單位實驗室的真實資料，來評估本研究之混合式方法的推薦效果，並與傳統的協同式過濾方法做比較。最後，實驗結果顯示，工作者對於知識文件的偏好與知識參考行為，可以有效地改善推薦品質並促進組織內的知識分享。 此外，為了協助群體學習與分享工作相關知識，針對以工作任務為基礎之群體，我們提出整合資訊檢索與資料探勘技術之演算法，發掘與建構群體知識流（Group-based Knowledge Flow）。群體知識流可利用有向性之知識圖來表示，藉此呈現一群工作需求相近工作者的知識參考行為（或知識流），而從知識圖中所發現的頻繁知識參考路徑，可以代表群體使用者的頻繁知識流。為了驗證方法的效能，我們實作一個群體知識流探勘之雛型系統。在一個重視協同合作與團隊合作的環境中，透過群體知識流探勘的方法與系統，可以加強組織學習，以及知識的管理、分享與再利用。

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. Additionally, knowledge 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. This work first 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. Finally, 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. Moreover, to support group-based learning and share task-related knowledge, we propose an algorithm that integrates 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. The frequent knowledge referencing path is identified from the knowledge graph to indicate the frequent knowledge flow of the workers. To demonstrate the efficacy of the proposed method, we implement a prototype of the GKF mining system. Our GKF mining method and system can enhance organizational learning and facilitate knowledge management, sharing, and reuse in an environment where collaboration and teamwork are essential.