Integrated semantic-based composition of skills and learning needs in knowledge-intensive organizations

Creation and maintenance of competencies is one of the most strategic activities in organizations, especially in knowledge intensive ones, as is the case of consulting companies. The role of core competencies in making a company achieve competitive advantage has been widely investigated in (Hamel and Prahalad, 1990). Other studies (Gronau and Uslar, 2004) show that the return on investment is significantly impacted by enriching knowledge management systems companies use, with components for the specific management of skills(Skill Management Systems, SMS). One of the services SMS should provide is the search for competencies inside the organization: an organization dealing with a task to perform will firstly check for the required skills among the available personnel, an activity whose complexity grows up with the size of the company. If such a search process leads to discover lack of needed competencies, organizations may hire external personnel, or encourage internal personnel to learn new competencies on the unavailable skills. The current availability of several, well-organized, e-learning modules, makes such possibility appealing and economically advantageous. A skill management system performing both the process of searching among available skills and facilitating the creation of missing ones can hence be a noteworthy source of competitive advantage for a knowledge-intensive organization. We present here an approach and a system for supporting the whole process of skills retrieval and creation inside a company: the proposed framework exploits recent advances in semantic-based inference services and technologies. It is noteworthy that the terms skill and competence are not kept distinct in the rest of the chapter: they both are meant to describe any ability or sort of knowledge held by individuals. The approach exploits the formalism and the reasoning services provided by Description Logics(DLs) and is fully in the Semantic Web initiative mainstream.