Enhanced Association Rules over Ontology Resources

Data mining has emerged to address the problem of drawing interesting knowledge from data. Among the most used data mining techniques, we concentrate on association rules which lead to the derivation of useful associations and correlations within data. In parallel, the advance of the ontology which is one of the most important concepts in knowledge representation has speedily altered the way of information structuring and sharing. Recently, the area of coupling association rules and ontology has been a focus for several researchers. In this paper, we aim to extract enhanced association rules over ontological resource. Thus, we introduce a new approach ECARD for an enhanced association rules derivation. Indeed, two main categories of knowledge are drawn, namely transitive and causal association rules. The encouraging carried out experimental results show the usefulness of our approach.