It is a well known fact that the data mining process can generate thousands of patterns from data. Various measures exist for evaluating and ranking these discovered patterns but often they don’t consider user subjective interest. We propose an ontology-based data-mining methodology called ExCIS (Extraction using a Conceptual Information System) for integrating expert prior knowledge in a data-mining process. Its originality is to build a specific Conceptual Information System related to the application domain in order to improve datasets preparation and results interpretation. This paper focus on our ontological choices and an interestingness measure IMAK which evaluates patterns considering expert knowledge.