In the knowledge-based economy, organizations often use expert finding systems to identify new candidates or manage information about the current employees. In order to ensure the required level of precision of returned results, the expert finding systems often benefit from semantic technologies and use ontologies in order to represent gathered data. Usage of ontologies however, causes additional challenges connected with the efficiency, scalability as well as the ease of use of a semantic-based solution. Within this paper we present a reasoning scenario applied within the eXtraSpec project and discuss the underlying experiments that were conducted in order to identify the best approach to follow, given the required level of expressiveness of the knowledge representation technique, and other requirements towards the system.