Abstract : In the Semantic Web context, OWL ontologies represent the con-ceptualization of domains of interest while the corresponding as-sertional knowledge is given by the heterogeneous Web resources referring to them. Being strongly decoupled, ontologies and assertion can be out-of-sync. An ontology can be incomplete, noisy and sometimes inconsistent with regard to the actual usage of its conceptual vocabulary in the assertions. Data mining can support the discovery of hidden knowledge patterns in the data, to enrich the ontologies. We present a method for discovering multi-relational association rules, coded in SWRL, from ontological knowledge bases. Unlike state-of-the-art approaches, the method is able to take the intensional knowledge into account. Furthermore, since discovered rules are represented in SWRL, they can be straightforwardly integrated within the ontology, thus (i) enriching its expressive power and (ii) augmenting the assertional knowledge that can be derived. Discovered rules may also suggest new axioms to be added to the ontology. We performed experiments on publicly available ontologies validating the performances of our approach.