3.1 Rationale to design task-based knowledge support
The proposed work focuses on providing knowledge support for knowledge-intensive tasks within organizations. Examples of knowledge-intensive tasks include thesis works and research projects in academic organizations, project management in firms, research work and product development in R&D departments, and the like. In such task-based environments, reusing knowledge assets extracted from historical task executions is the key to providing effective knowledge support for conducting tasks.
Historical codified knowledge, i.e. experiences and know-how extracted from previous task executions, provides valuable knowledge for conducting tasks. For example, effective project management can benefit from KMS by referring similar projects to acquire best practice, lessons learned, working experiences, or knowledge resources. Research task innovation is generally based on previous research achievements. A knowledge repository that preserves the experience and knowledge of previous work (research task) is important to provide effective knowledge support for research tasks. However, with the increasing amount of information in the organizational memory (OM), contemporary KMS faces challenge to assist organizations acquire, organize and manage knowledge. Thus, delivering relevant historical codified knowledge to workers for accomplishing tasks at hand is also a challenging work deserves exploration. This work sought to tackle the challenges from the perspective of business task.
3.1.1 Task-based organizational environment
“Mary is a new worker of an industry analyzer in a project management institution.
She is assigned to a survey task, “the opportunities of sensor network in healthcare”,
and need to write a proposal. Since Mary is a novice of sensor network, she faced the
problem to understand the assigned task. She wants to find task-related expert or
colleague to solve the encountered problem or guide him to the right direction while
understanding the perceived task. Unfortunately, workers who have relevant
knowledge are busy for the business projects. Hence, Mary comes up with the idea to
find the possible solutions from the document management system or information
repository in the organization. However, tremendous amount of data frustrated Mary.
That is, it is hard for Mary to have a clear view of information structure or taxonomy of the document management system or information repository.”
The situation generally happens in the organization, especially in IT or MIS department of industry, or the industry analyst in project management institution.
When a worker in an organization has information needs of the executing task, he/she might need the knowledge support to accomplish the task. Naturally, the worker may seek someone who has met this problem or has done similar experiences before. Otherwise, the worker may also try to find the relevant codified knowledge from the organizational repository. Thus, if knowledge resources in an organization are acquired, organized via the view of business tasks, workers could get more effective knowledge support.
3.2 Framework of task-based knowledge support
Figure 2 illustrates the system framework of the proposed task-based knowledge support based on profiles to facilitate task-based knowledge delivery and sharing.
Participants include knowledge workers engaged in specific tasks and domain experts in specific subjects. The system comprises four main modules, namely
task-oriented information repository, task profile handler, task-needs evolution, and task-oriented information service router.
Task-oriented information repository. The task-oriented information repository is designed for organizing and managing task relevant information. Building a proper repository to acquire and disseminate knowledge items is a key strategy for managing knowledge in the contemporary KMS. Information items indexed by proper concepts and categories can provide knowledge workers with meaningful access to organize intellectual content. Task-oriented repositories are constructed with support from category schema to effectively utilize codified knowledge. Such a repository stores codified knowledge corresponding to task execution, and contains three main databases, including the document-indexing database, task corpus, and
task categorization database. The document-indexing database stores task relevant
documents indexed using the inverted file approach. Meanwhile, the task corpus stores the key profile of each existing task. An existing-task is a historical task accomplished within the organization. Task corpus is used to describe the keyDomain Ontology Codified Knowledge and Human
Resource Support
Fig. 2. Framework of task-based knowledge support
subjects of an existing task, and is expressed as a feature vector of weighted terms.
Section 4.1 details the extraction of task corpus of an existing task, which is derived by extracting the weighted terms from textual documents generated and accessed by the task. Moreover, the task categorization database records the relationships of existing tasks and categories, namely, the relevance degrees of existing tasks to categories. The task categorization database is used to support the operation of identifying referring tasks based on their similarity to the executing task derived using the relevance degrees of tasks to the categories. Moreover, tasks with similar subjects are grouped into fields. The repository is the knowledge base for task-based knowledge support. Details are discussed in Chapter 4.
Task profile handler. The task profile handler provides mechanisms such as profile creation, adjustment, integration and profile adaptation to conduct profile management. Two kinds of profiles, feature-based task profile and topic-based task profile, are maintained to model workers’ information needs on the target task at hand.
y
Feature-based task profile describes the key features of a task and is the kernel
for discovering and disseminating task-relevant information to knowledge workers.y
Topic-based task profile models a worker’s information needs on the target
task, and is represented as a set of relevant tasks or fields of the target task with associated relevance degrees.Workers’ information needs may change during the progress on performing the target task. The user behavior tracker is an on-line module to capture workers’
dynamic behaviors, including access behaviors on the task-based domain ontology and relevance. The profile handler uses an adaptive task-based profiling approach to adjust workers’ profiles. The peer-group analyzer employs a task-based peer-group analytical method to identify peer-groups with similar task needs (information needs on the target task) based on work profiles. Details will be addressed in Chapter 6.
Task-needs evolution. The task-stage identifier and task-needs analyzer are within this module, which are responsible for tracking the evolution of a worker’s task-needs. The profiles are employed as indicators for task-stage identifier and
task-needs analyzer to model the worker’s task-needs of target task. Herein, worker’s
task-needs are modeled as the topics nodes in domain ontology (DO) at different abstraction level which are relevant to the on-going task. The DO is a multi-level structure and each node in the DO represents a research topic in our application domain, as given in the Figure 3 of Chapter 4. The task-stage identifier is responsible for analyzing and determining worker’s task stage based on the changes of the task profile over time. The task-needs analyzer is responsible for tracking the worker’s access behavior over a period of time. The access behavior is analyzed based on the DO to discover worker’s task-needs on specific topics. Details are discussed in Chapter 7.Task-oriented information service router. The router helps knowledge workers gather appropriate information from the task-oriented repository and task-based peer-groups. The router fetches task-relevant information according to the worker’s task profile. Moreover, each worker has his/her own view of task-relevant information, namely, personalized ontology, which is derived from his/her work profile on the target task and is organized according to the domain ontology.
Knowledge sharing from other peer-group members is derived by retrieving each peer-group member’s personalized ontology. Details are addressed in Chapter 6.