Knowledge management systems (KMS) have been researched and developed to manage valuable information assets in organizations. Knowledge can be categorized into two types, explicit and implicit. While managing explicit knowledge is comparatively easy using information technology such as databases, implicit knowledge, which are usually embedded in the operating procedures as routines or standards in the organizations is hard to be systemized. In the highly competitive business environments, organizations are required to manage their tacit knowledge to maintain their core competencies and even to survive. Not appropriately managing tacit knowledge results in (1) the loss of valuable organizational knowledge with the departure of its holders and (2) inefficient allocation of organizational resources.
Even though its strategic importance is sufficiently acknowledged by knowledge management researchers and practitioners, tacit knowledge is very much difficult to be managed easily due to its semi- or unstructured characteristics. Even though much research effort has been contributed to develop ways to codify and store implicit knowledge the same way manipulating its counterpart, explicit knowledge, researches in this direction have failed to be widely accepted in industry due to its limited applicability. Recent researches in knowledge management systems have shown that it is more applicable in many ways to provide expert search mechanisms in KMS to pinpoint experts in the organizations with searched expertise so that users can contact them for help.
In this research, we develop a framework to automate expert profiling using text categorization to minimize maintenance cost of manual expert profiling process while eliminating possible incorrectness or obsolescence resulted from subjective manual processing. To do so, we develop the structure of expertise consisting of activeness, relevance, and usefulness factors to enable deriving the overall expertise lev...