Chapter 9 Conclusions and Future Works
9.2 Future works
This work focuses on providing knowledge support for knowledge-intensive tasks such as thesis works, research projects, project management, and product development. Issues along with the research direction will be addressed.
Context awareness knowledge support: Although we provide a task-view to achieve knowledge support, our current work does not consider the process-aspect and context awareness, as discussed in [1][2][22][24][44]. The process knowledge supports the operations of workflow management systems to manage business processes. The context-based knowledge support utilizes the context of activities, roles, work-related skills, and so on to provide context-aware knowledge access and retrieval. Future studies could extend the proposed approach to support context-aware or process-aware delivery of task-relevant knowledge. Moreover, the information needs of knowledge workers are associated with their roles in undertaken tasks; however, this work does not consider the role/job perspective [5][72] to acquire and disseminate task-relevant knowledge. Future studies could extend the proposed profiling approach by considering role/task to acquire and reuse corporate memory effectively.
Refinement of task domain ontology: In this project, we refer the domain ontology to a classification structure of tasks stored in the information repository [33]
[52][56]. Specifically, the domain ontology (DO) is a simple topic taxonomy that is
structured into four levels, including categories, fields, tasks and knowledge items. In
the future, we shall extend our domain ontology to. In the area of knowledge management, the domain ontology also can be expressed as structured link networks are frequently used to represent the organization’s knowledge [74]. Besides the topic taxonomy organized in our problem domain, the ontology underlying the K-Support knowledge support portal could be extended to represent the organization’s domain-specific knowledge to conduct knowledge support by the utilization of task associated context. The ontological structure can be further extended to specify the knowledge concepts, properties of each concept and semantic relationship between concepts in organizations.Mining and recommendation techniques in supporting task-relevant knowledge: Due to the limitation of this work, the task-related experts of each task are predefined, as addressed in Section 5. In addition, the relative importance of experts is given the same weight to aggregate the relevance ratings. In the future, we shall consider revising our group decision method with the aid of recommendation and mining techniques in Recommender system. In fact, our lab have investigated the document recommendation in organization with personal folders [36]. That is, we adopt recommendation techniques to provide knowledge workers needed textual documents from other workers folders. Thus, various recommendation methods have been evaluated to analyze the tradeoff between methods. Accordingly, we will employ methods, e.g., collaborative filtering algorithm, demographic profiles of workers, etc, to determine task-related experts and resolve the cold-start problem in system. Thus, the new-system cold-start problem may be resolved by the demographic profiles of workers and the new-user cold-start problem my be resolved by the hybrid recommendation technique.
Moreover, in our on-going work [48], we proposed a task-stage mining method for discovering task-stage needs from historical task sets. Thus, the valuable pieces of knowledge items can be extracted from the mining result. In the future, we will maintain the task-relevant knowledge as the meta knowledge to extend the system capability of finding more task-relevant knowledge by utilizating the context of business historical task. Meanwhile, we will also integrate the proposed task-stage mining technique with our task-stage identification model to investigate the contribution of the task-stage knowledge support model empirically [87].
Computer supported collaborative work: Furthermore, this work focuses on generating task profiles by the collaboration of knowledge workers to analyze the relevance of tasks and codified knowledge. Our work is further enhanced to develop a knowledge support (K-Support) system which can stimulate knowledge sharing among task-based peer-groups. Although the K-Support system can provide collaborations among knowledge workers through collaborative assessment and knowledge sharing, more computer supported collaborative work (CSCW) is required for successful accomplishment of tasks, especially for complex and volatile tasks. In CSCW environments, groupware is often employed to support collaboration, coordination and communication among groups of people. Notably, this work concentrates on providing task-relevant knowledge without exploring CSCW issues.
Future work will integrate our proposed work with CSCW technology to provide more effective supports for collaborations among knowledge workers. Moreover, some tasks may span across different organizations so that inter-organizational collaboration between knowledge workers is required. This work does not consider inter-organizational collaborations. Further issues regarding this aspect need to be investigated, such as reusing and exchanging task-relevant knowledge across organizations.
Application domain: The proposed task-based knowledge support model may be tailored to other application domain in supporting the execution of long-term knowledge-intensive task-execution. For example, the R&D related work such as project management, intellectual property management, academic researches and industry analysis. We will also seek the other possible application domain to apply the proposed model such as the industry analysis in the project management institution, product development in R&D department and so on.
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