1.1 Research background and motivation
Deploying knowledge management systems (KMS) is an important strategy for enterprises to effectively managing business knowledge and gaining competitive advantage. The operations and management activities of enterprises are mainly based on tasks, in which organizational workers perform various tasks to achieve business goals [1][22][24][26]. Moreover, organizations try to maximize the use of knowledge assets to increase an organization’s profitability and productivity with the support of contemporary knowledge management tools. KMS employs Information Technologies (IT), such as document management and workflow management to facilitate the access, reuse and sharing of knowledge assets within and across organizations [17][39]. That is, the critical role of Information Technologies (ITs) is to assist knowledge workers to reuse valuable knowledge assets to carry out business tasks successfully [6][17][46].
Generally, ITs focus on explicit and tacit dimensions in knowledge management activities [28][39]. The former, explicit knowledge management, is achieved by a codified approach. Intellectual content codified into explicit form can facilitate knowledge retrieval and reuse [89]. Knowledge repository, knowledge-based systems, and knowledge maps are the supports for knowledge storage, organization and dissemination. And a repository of structured and explicit knowledge, especially in document form, is a widely adopted codification-based strategy for managing knowledge in KMSs [17][81][89]. The latter, tacit knowledge management, puts emphasis on dialoging via social networks to facilitate knowledge sharing.
Knowledge expert directories, yellow pages, communities of practices and talk rooms, support interpersonal communication for knowledge sharing [3][41]. Notably, empirical findings indicate that codifying intellectual content into a knowledge repository makes workers highly exploit existing organizational resources [29][49].
Accordingly, knowledge (information) retrieval is considered a core component to retrieve codified knowledge in KMS. An effective knowledge retrieval function can mitigate the difficulty of accessing knowledge items from a knowledge repository and support the operation of knowledge-intensive work in business environments [24][27].
In task-based business environments, an important issue of deploying KMS is providing task-relevant information (codified knowledge) to fulfill the information needs of knowledge workers during task execution. That is, effective knowledge management relies on understanding workers’ information needs on tasks, for brevity, task-needs. Recently, the information retrieval (IR) technique coupled with workflow management systems (WfMS) was employed to support proactive delivery of task-specific knowledge according to the context of tasks within a process [1][2][23][24]. The KnowMore system maintains task specifications (profiles) to specify the process-context of tasks and associated knowledge items [1][2]. The Kabiria system supports knowledge-based document retrieval in office environments, allowing users to conduct document retrieval according to the operational context of task-associated procedures [15]. Context-aware delivery of task-specific knowledge thus can be facilitated based on the task specifications and current execution context of the process. Furthermore, a process meta-model specifying the knowledge-in-context is integrated with workflow systems to capture and retrieve knowledge within a process context [44]. Although providing an appropriate view for designing task-based knowledge support, the above works focus on specifying the process-context of tasks to support context-aware or process-aware knowledge retrieval, rather than on a systematic approach to construct task profiles. Moreover, the adaptation of profiles to track workers’ dynamic information needs is not addressed.
For complex and knowledge-intensive tasks, the collaboration among knowledge workers may arise around common goals, problems and interests. Accordingly, contemporary KMSs rely on an effective approach to construct a community of practice to promote knowledge sharing. A community of practice consists of people who share common needs of information; hence, a community of practice is an effective approach to promote knowledge creation, transfer and sharing within or across organizations [3][13][18][41]. The Milk system supports informal communication and knowledge sharing for knowledge workers performing tasks in different work practices [3]. OntoShare, an ontology-based KMS, models the interests of users and provides automatic knowledge sharing in communities of practice with the aid of profiles [18]. Although user profiles had been employed to stimulate knowledge disseminations in communities of practice, they did not
consider the identification of peer-groups with similar task-needs to form communities in the task-based business environment.
Furthermore, for knowledge-intensive tasks, such as research projects in academic institutions, and product development in R&D departments, it is more difficult to supply task-relevant knowledge during the progress of task execution. That is, works’
information needs on task, for brevity, task-needs, generally change during the long run of task performance. Thus, the issues of identifying and tracking workers’ current task-stages and task-needs topics, and adjusting their profiles during task performance deserve further exploration. To provide a more effective long-term knowledge support, we propose a task-stage knowledge support model that incorporates Information Filtering model with the identification of worker’s task-stage and task-needs topics.
1.2 Research objectives and tasks
This dissertation mainly investigates the issues related to delivering and sharing codified knowledge from the perspective of business task. Major research objectives are listed below.
(1) Proactively delivering task-relevant knowledge to workers engaged in knowledge-intensive tasks.
• A task-relevance assessment approach is proposed to identify workers’
information needs on task.
• A task-based knowledge support model is proposed to track and model workers’ dynamic information needs on task. The proposed model also promotes knowledge sharing among knowledge workers.
(2) Enhancing task-based knowledge support model to provide effective knowledge support at different task-stages
• Developing a task-stage knowledge support model to provide task-relevant knowledge according to workers’ dynamic task-needs at different task stages.
• Also, employing user modeling technique to identify worker’s task-stage and task-needs topics of stages.
(3) Deploying a task-based K-Support portal to acquire, organize, and
disseminate the organization’s knowledge resources from the aspect of task.
• Providing a collaborative task-based workplace to facilitate knowledge retrieval and sharing among peer-groups.
• Delivering and sharing task-relevant knowledge to fulfill the workers’
task-needs at various task-stages.
1.3 Contributions
The contribution of this dissertation is to achieve knowledge reuse and support from the perspective of knowledge-intensive task. That is, extracting, organizing, and disseminating relevant knowledge (codified knowledge) to fulfill the information needs of knowledge workers during task execution.
This work first proposes a novel task-relevance assessment approach to identify the knowledge worker’s information needs on tasks. Rather than specifying task characteristics directly by knowledge workers, a systematic approach is desirable to create task profiles by analyzing retrieved documents and assessing the relevance among tasks. Note that historical task-related information items preserved in the knowledge repository, such as task descriptions and codified knowledge, are valuable knowledge assets to support task profile construction. The proposed approach generates task profiles by the collaboration of knowledge workers to analyze the relevance of tasks and codified knowledge. Task-based knowledge support is facilitated through providing knowledge workers relevant knowledge based on task profiles. Although this work does not consider the process-aspect and context awareness, as discussed in previously pilot studies [1][2][24][44], this approach can alleviate the problem of accessing needed knowledge items from vast amounts of codified knowledge.
Furthermore, methods of the adaptation of profiles to track workers’ dynamic information needs are proposed in this work. The worker’s dynamic task-needs can be analyzed based on the changes of workers’ profiles during task performance. An
adaptive task-based profiling approach is proposed to tackle worker’s dynamic
information needs on tasks. A task profile describes the key features of a task and is the kernel for discovering and disseminating task-relevant information to knowledge workers. This approach models the worker’s task-needs based on feedback analysis, i.e. explicit or implicit feedback on knowledge items. In addition, this work not only considers the profiles of feedback items but also considers the profiles of relevanttopics in the domain ontology. Note that we refer the domain ontology as the taxonomy of topics in our task-based problem domain. Different from traditional information filtering techniques with user profile, which only considered the profile of feedback items, the profile adaptation approach considers both the profiles of related tasks and the profiles of relevant codified knowledge to adjust the task profile.
For promoting knowledge sharing among workers, a task peer-group analytical
method is proposed to identify task-based peer-groups according to workers’ profiles,
namely, task interests. The main characteristic of this method is that a fuzzy inference procedure is employed to infer the implicit and transitive relationships of knowledge workers based on task-needs. The proposed method can infer the implicit relationship among workers; even they did not provide feedback on the same knowledge items. With the aid of task-based profiles and peer-groups, the proposedK-support portal can provide task-relevant knowledge and promote knowledge
sharing among task-based peer-groups.Moreover, according to our empirical investigation, knowledge workers engaged in knowledge intensive tasks (e.g., research projects in academic organizations, project management in firms, etc.) have different information needs during the long-term task performance. The Vakkari studies (2000, 2003), which focus on a user’s information seeking activities during task performance (e.g., writing a proposal, completing a project, etc.), show that information needs vary according to different task stages. Therefore, we propose a knowledge support model based on task-stage to proactively deliver task-relevant knowledge. A correlation analysis
method is proposed to identify a worker’s task-stage (e.g., pre-focus, focus
formulation, and post-focus task stages), and an ontology-based topic discoverymethod is proposed to determine a worker’s task-needs topics of each stage.
Consequently, the model can also be tailored to support long-term task performance.
Finally, we develop a collaborative task-based K-support portal to facilitate knowledge reuse and to further promote knowledge sharing among peer-groups. The view of designing task-based knowledge support is the studies of context-aware or process-aware knowledge retrieval and knowledge delivery with the aid of user modeling. Details will be given in Section 3. Meanwhile, several experiments have been conducted to evaluate the effectiveness of the proposed knowledge support model
based on task or task-stage in terms of precision and recall. The empirical system evaluation is also conducted to examine the effectiveness of the proposed system in terms of novelty and quality metrics.
1.4 Content organization
Fig. 1 illustrates the whole view of this work and the remainder of this work is organized as follows. The literature review is given in Chapter 2. Chapter 3 addresses the rationale to design task-based knowledge support system and presents the framework of the proposed system. Note that the tasks and functions of each module given in Fig. 1 are described in this section. Chapter 4 introduces the process of building the task-oriented repository, as depicted in the block one (B1) of Fig.1. The repository is designed for organizing and managing task-relevant information. In addition, a task domain ontology is structured to organize and classify knowledge items based on tasks.
The K-support model and methods to provide task-based knowledge support with the aid of profiling technique are given in Chapter 5, 6, and 7. Note that the associated experiments to evaluate the effectiveness of the proposed methods are also carried out. Chapter 5 presents the proposed task-relevance assessment
approach to identify the worker’s information needs on tasks. The task-relevance
assessment approach is designed to analyze the relevance of tasks and codified knowledge in the repository. Furthermore, a task profile is generated to support the proactive delivery of task-relevant knowledge. The assessment procedure is also given in the block two (B2) of Fig. 1. The lines with the numbers denote the assessment procedure. Next, Chapter 6 describes the proposed methods to disseminate and share task-relevant knowledge based on the generated profiles. The block three (B3) of Fig. 1 illustrates the main executed engines of Chapter 6 & 7.The user behavior tracker is an on-line module to capture workers’ dynamic behaviors, including access behaviors on the task-based domain ontology and documents. The task profile handler uses task-based profiling approach to adjust workers’ task profile to reflect workers’ current task-needs (information needs on the target task). The peer-group analyzer employs peer-group analytical method for identifying task-based peer-groups with similar task needs based on task profiles.
Details will be addressed in Chapter 6. Chapter 7 extends the task-based knowledge support model to provide effective knowledge support at different task-stages The task-stage identifier and task-needs analyzer are within the block three (B3), which are responsible for tracking the evolution of a worker’s task-needs. Methods to
identify worker’s task-stage and task-need topics of stages are presented in this chapter.
Finally, the proposed
K-Support portal with associated system evaluation is
presented in Chapter 8. Conclusions and future works are discussed in Chapter 9.Fig. 1. Task-based knowledge support