This paper deals with knowledge integration in a data mining process. We suggest to model domain knowledge during business understanding and data understanding steps in order to build an ontology driven information system (ODIS). We present the KEOPS Methodology based on this approach. In KEOPS, the ODIS is dedicated to data mining tasks. It allows using expert knowledge for efficient data selection, data preparation and model interpretation. In this paper, we detail each of these ontology driven steps and we define a part-way interestingness measure that integrates both objective and subjective criteria in order to evaluate model relevance according to expert knowledge.