Nowadays, the scientific community is more and more interested by the mediation problem within Peer-to-Peer (P2P) systems and by data sources migration within the semantic web. Data integration and interoperability become a necessity to meet the need for information exchange between heterogeneous information systems. They reflects the ability of an information system to collaborate with other systems sometimes of a very different nature and aims at developing architectures and tools for sharing, exchanging and controlling data. In this context we have proposed a new heterogeneous and distributed data management system in a P2P environment called MedPeer. Among this system functions, we have focused in this article on relational databases description through the use of ontologies. We thus propose Relational.OWL2E, a new approach that, starting from the relational schema, generates an ontology based on the OWL2 language. Our main contribution lies in the semantics we have added to relational databases concepts in representing attributes by rich XML schema datatypes, primary keys, unique keys, foreign keys and by associating to each class a set of synonyms in order to guide the process of discovering semantic correspondences.

Many large organizations have multiple databases distributed over different branches. Number of such organizations is increasing over time. Thus, it is necessary to study data mining on multiple databases. Most multi-databases mining (MDBM) algorithms for association rules typically represent input patterns at a single level of abstraction. However, in many applications of association rules – e.g., Industrial discovery, users often need to explore a data set at multiple levels of abstraction, and from different points of view. Each point of view corresponds to set of beliefs (and representational) commitments regarding the domain of interest. Using domain ontologies, we strengthen the integration of user knowledge in the mining and post-processing task. Furthermore, an interactive and iterative framework is designed to assist the user along the analyzing task at different levels. This paper formalizes the problem of association rules using ontologies in multi-database mining, describes an ontology-driven association rules algorithm to discoverer rules at multiple levels of abstraction and presents preliminary results in petroleum field to demonstrate the feasibility and applicability of this proposed approach.