Creating graphical models as representation of personalized skill profiles

In this paper we address and discuss the problem of creating graphical models as representation format of personalized skill profiles in different application environments (e.g. skill matching for staff positioning in a project). We can learn employee skills from data based on structured profiles and their representation as Bayesian network structure using an information theoretic dependency analysis approach. Many enterprise resource management systems (ERP) come along with integrated modules for Human Resource Management (HRM). One main task of HRM is to manage, improve and deploy the right skills at the right time. These processes are well known as skill management. Furthermore the problem of finding dependencies between employee skills not obvious in evidence is considered. Using an information theoretic approach to construct a powerful skill representation as graphical model is comprehensible. To demonstrate the achievement of the learned and created network structure, a test scenario concerning historical reference project data is given.