Leveraging Corporate Skill Knowledge - From ProPer to OntoProPer

Skill management systems serve as technical platforms for mostly, though not exclusively, corporate-internal market places for skills and know-how. The systems are typically built on top of a database that contains profiles of employees and applicants. Thus, the skills may be retrieved through database queries. However, these approaches incur two major problems, viz. the finding of approximate matches and the maintenance of skill data. In this paper we describe two systems that leverage corporate skill knowledge by offering advanced means for both. We present ProPer that uses means from decision theory to allow for compensate skill matching. Then, we describe OntoProPer that combines these methods with intelligent means for inferencing of skill data. For the latter an ontology provides background knowledge, i.e. conceptual structures and rules, which supplement the skill database with ground and inferred facts from secondary information, such as project documents. These supplement facts reduce maintenance efforts since much secondary information is gathered in the organizational memory through common working tasks.