Abstract

The operations and management activities of enterprises are mainly task-based and knowledge intensive. Accordingly, an important issue in deploying knowledge management systems is the provision of task-relevant information (codified knowledge) to meet the information needs of knowledge workers during the execution of a task. Codified knowledge extracted from previously executed tasks can provide valuable knowledge about conducting the task-at-hand (current task), and is a valuable information source for constructing a task profile that models a worker's task needs, i.e., information needs for the current task. In this paper, we propose a novel task-relevance assessment approach that evaluates the relevance of previous tasks in order to construct a task profile for the current task. The approach helps knowledge workers assess the relevance of previous tasks through linguistic evaluation and the collaboration of knowledge workers. In addition, applying relevance assessment to a large number of tasks may create an excessive burden for workers. Thus, we propose a novel two-phase relevance assessment method to help workers conduct relevance assessment effectively. Furthermore, a modified relevance feedback technique, which is integrated with the task-relevance assessment method, is employed to derive the task profile for the task-at-hand. Consequently, task-based knowledge support can be enabled to provide knowledge workers with task-relevant information based on task profiles. Empirical experiments demonstrate that the proposed approach models workers' task-needs effectively and helps provide task-relevant knowledge.

Keywords: Information retrieval; Knowledge management; Relevance assessment; Task-based knowledge support; Task profile

Article Outline

1. Introduction

2. Task-based knowledge support

2.1. Overview of task-based knowledge support

2.1.1. Task relevance assessment

2.1.2. Generating task profiles by modified relevance feedback techniques

2.2. System architecture of task-based knowledge support

2.2.1. Process of task-based knowledge support

3. Preliminary techniques

3.1. Information retrieval in a vector space model

3.1.1. Similarity measure

3.2. Relevance feedback techniques

3.3. Modeling user perceptions by a fuzzy linguistic approach

4. Task-oriented information repository

4.1. Extracting the task corpus

4.2. Task categorization model

4.2.1. Deriving the feature vector of each category

4.2.2. Deriving the degree of relevance of executed tasks to categories

5. Task relevance-assessment and knowledge retrieval

5.1. Two-phase relevance assessment based on the fuzzy linguistic approach

5.1.1. Phase 1: Identifying reference tasks based on category assessment

5.1.1.1. Step 1: Determining the semantic term set and corresponding fuzzy number

5.1.1.2. Step 2: Collaborative assessment of the relevance of tasks to categories

5.1.1.3. Step 3: Aggregating the relevance ratings of evaluators

5.1.1.4. Step 4: Selecting reference tasks

5.1.2. Phase 2: Assessing the relevance of reference tasks

5.2. Constructing the task profile based on relevance feedback

5.3. Task-based knowledge retrieval

5.3.1. Relevant tasks and peer group recommendations

5.3.2. Relevant documents and term recommendation

6. Experiment evaluations

6.1. Experiment setup

6.1.1. Experiment objectives

6.1.2. Data and participants

6.1.3. Performance evaluation metrics

6.1.4. Parameter selection

6.2. Experiment results

6.2.1. Experiment one: effect of fuzzy linguistic assessment

6.2.1.1. Observations and implications

6.2.2. Experiment two: effect of two-phase relevance assessment

6.2.2.1. Observations and implications

6.2.3. Experiment three: effect of collaborative assessment

6.2.3.1. Observations and implications

6.2.4. Discussion

7. Conclusions and future work

Acknowledgements

Appendix A. Fuzzy numbers

Appendix B. Relevance assessment by linguistic ratings

References

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