1.1 Research Background and Motivation
Organizational knowledge can be used to create core competitive advantages and achieve commercial success in a constantly changing business environment. Hence, organizations need to adopt appropriate strategies to preserve, share and reuse such a valuable asset, as well as to support knowledge workers effectively [42, 44]. Knowledge and expertise are generally codified in textual documents, e.g., papers, manuals and reports, and preserved in a knowledge database. This codified knowledge is then circulated in an organization to support workers engaged in management and operational activities [12]. Because most of these activities are knowledge-intensive tasks, the effectiveness of knowledge management depends on providing task-relevant documents to meet the information needs of knowledge workers.
In task-based business environments, knowledge management systems (KMSs) can facilitate the preservation, reuse and sharing of knowledge. Moreover, workers may need to obtain task-relevant knowledge to complete a knowledge-intensive task by referencing codified knowledge (documents); For example, based on a task’s specifications and the process-context of the task, the KnowMore system [1] provides context-aware knowledge retrieval and delivery to support workers’ procedural activities. The task-based K-support system [39, 58] adaptively provides knowledge support to meet a worker’s dynamic information needs by analyzing his/her access behavior or relevance feedback on documents.
To help knowledge workers complete multiple tasks, TaskTracer [19] was developed to monitor workers’ activities and help them rapidly locate and reuse processes employed previously. However, previous research on task-based knowledge support did not analyze and utilize the flow of knowledge among various types of codified knowledge (documents) to provide effective recommendations about task-relevant documents.
Knowledge flow (KF) research focuses on how KF can transmit, share, and accumulate knowledge when it passes from one team member/process to another. In a workflow situation, work knowledge may flow among workers in an organization, while process knowledge may flow among various tasks [61-62, 64]. Thus, KF reflects the level of knowledge cooperation
between workers or processes and influences the effectiveness of teamwork/workflow. Zhuge [61] proposed a management mechanism for realizing ordered knowledge sharing, and integrated the knowledge flow with the workflow to assist people working in a complex and knowledge intensive environment. Also, KF plays an important role in academic research, as researchers often devise novel concepts based on previous research reported in the literature [63]. However, to the best of our knowledge, there is no systematic method that can flexibly identify KF in order to understand the information needs of workers. Furthermore, conventional KF approaches do not analyze knowledge flow from the perspective of information needs and recommend relevant documents based on the discovered KF.
Knowledge workers normally have various task needs over time. Moreover, they may need to obtain task-relevant knowledge to complete a task by referencing several types of codified knowledge (documents); and the knowledge in one document may prompt a worker to reference another related document. Based on a worker’s referencing behavior, KF can be used to describe the evolution of information needs, preferences, and knowledge accumulated for a specific task. From the perspective of information needs, some knowledge in a KF may have a higher priority for accomplishing a task. For example, before taking a Data Mining course, a student must take courses in Statistics and Database Systems, which represent the fundamental knowledge of Data Mining. Thus, these two courses are significant and have a high priority for the student. Additionally, academic knowledge may flow between different courses and thereby help students accumulate more knowledge. Similarly, the codified knowledge for a task also has different referencing priorities and ordering based on its perceived importance. In other words, important basic knowledge about a task should be referenced first. Therefore, KF can be utilized to provide effective recommendations about task-relevant knowledge to suit workers’ information needs for tasks. This issue has not been addressed by previous research.
In task-based business environments, large amounts of such codified knowledge are circulated and accumulated in an organization to support knowledge workers engaged in diverse tasks and activities. Knowledge workers may cooperate with each other to accomplish a specific task. During the collaboration phase, task knowledge can be transmitted, shared and accumulated from one team member/process to another. Knowledge flows (KFs) can be used
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to represent the long-term evolution of workers’ information needs [36]. Based on those needs, the knowledge flow-based document recommendation method proactively delivers task-relevant topics and documents to the workers.
To work more efficiently, workers who have task-related knowledge, expertise and experience may join a task-based group and collaborate to perform a task. The workers can share task-related knowledge delivered by their knowledge flows (KF) during the collaboration. In addition, workers in the same group may have similar referencing behavior and techniques for learning knowledge. Each group may require knowledge of different topic domains to accomplish its tasks and goals. Because the information needs of workers or groups may change over time, modeling the knowledge referencing behavior of a group of workers is difficult. Obviously, recognizing those needs, delivering knowledge during the collaboration, and facilitating knowledge sharing/reuse are important issues that must be addressed in a knowledge intensive organization. However, to the best of our knowledge, there is no appropriate approach for analyzing and constructing KFs from the perspective of a group’s information needs; and very little research effort has been expended on KF mining for task-based groups.
1.2 Research Objectives
According to the research motivation, the major research objectives are listed below.
z Mining the knowledge flow for each knowledge worker and a group of workers;
z Identifying and analyzing topics of interest, major referencing behavior patterns, and the long-term evolution of workers’ information needs;
z Providing knowledge support adaptively based on the referencing behavior of workers;
z Effectively recommending task-relevant knowledge to suit workers’ information needs for tasks;
z Enhancing organizational learning and task collaboration;
z Facilitating knowledge dissemination, sharing and reusing among workers in the context of collaboration and teamwork;
1.3 The Approaches Based on Knowledge Flow
In an attempt to resolve the limitations of previous research, we first propose KF-based recommendation methods for recommending task-related codified knowledge. To adaptively provide relevant knowledge, collaborative filtering (CF), the most frequently used method, predicts a target worker’s preference(s) based on the opinions of similar workers. However, the target worker’s referencing behavior may change over the period of the task’s execution, because his/her information needs may vary. Traditional CF methods only consider workers’
preferences for codified knowledge. They neglect the effect of the time factor, i.e., workers’
referencing behavior for knowledge over time. To fill this research gap, we propose a KF-based sequential rule method (KSR) that recommends codified knowledge by utilizing the KF-based sequential rules. However, the method is based on the target worker’s referencing behavior without considering the opinions of his/her neighbors who may have similar preference for documents. Therefore, to take advantage of the merits of typical CF and KSR methods, we propose hybrid recommendation methods that combine CF and KSR methods to enhance the quality of document recommendation. The hybrid methods consider workers’
preferences for codified knowledge, as well as their knowledge referencing behavior, in order to predict topics of interest and recommend task-related knowledge.
The proposed hybrid methods consist of two phases: a KF mining phase and a KF-based recommendation phase. To determine a knowledge worker’s referencing behavior, the KF mining phase analyzes his/her historical work records to identify the knowledge flow, i.e., the target worker’s information needs. Then, the KF-based recommendation phase selects and recommends documents based on the document preferences and KF-based sequential rules derived from the target worker’s neighbors. In other words, the proposed methods trace a worker’s information needs by analyzing his/her knowledge referencing behavior for a task over time, and also proactively provide relevant codified knowledge for the worker based on the KFs of the worker’s neighbors.
According to the KF mining approach [36], we extend it and propose algorithms that integrate information retrieval and data mining techniques for mining and constructing the group-based knowledge flows (GKFs). Specifically, we discover a group’s KF from the KFs of the participating workers. First, based on the workers’ logs, we analyze each worker’s
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referencing behavior when acquiring task-related knowledge, and then construct his/her KF.
Workers who have similar KFs are clustered into the same group by a clustering method, and the resulting group is regarded as a working group. Because workers in the same group may adopt different behavior when referencing task-related knowledge, we design GKF mining algorithms to discover the frequent referencing behavior of a group of workers. Second, we apply the concepts of graph theory to visualize the GKF as a knowledge graph in which a vertex and an edge indicate, respectively, a topic domain and a direct flow relation between two topic domains. From the knowledge graph, frequent knowledge paths (patterns) can be identified based on the edge frequencies in the graph. The paths represent the worker’s frequent knowledge referencing behavior and important knowledge flows in the group.
Finally, to demonstrate the efficacy of our proposed method, we implement a prototype system for mining the GKF of a group of workers. The system provides useful functions that allow users to simplify the complexity of KF mining and visualize KFs graphically.
1.4 Organization of the proposal
The remainder of this proposal is organized as follows. Chapter 2 provides a brief overview of related works. In Chapter 3, we describe the knowledge flow model, the overview of knowledge flow-based research and the knowledge flow mining phase. The knowledge flow-based recommendation framework is illustrated in Chapter 4. The group-based knowledge flow mining methods are illustrated in Chapter 5. According to these methods, we propose a prototype system for mining the group-based knowledge flow. Finally, in Chapter 6, we summarize our conclusions and consider future research directions.