Abstract

This paper presents a hybrid, extensional and asymmetric matching approach designed to find out semantic relations (equivalence and subsumption) between entities issued from two textual taxonomies (web directories or OWL ontologies). By using the association rule paradigm and a statistical measure developed in this context, this method relies on the following idea: “An entity A will be more specific than or equivalent to an entity B if the vocabulary (i.e. terms and data) used to describe A and its instances tends to be included in that of B and its instances”. This matching approach is divided into two parts: (1) The representation of each entity by a set of relevant terms and data; (2) The discovery of binary association rules between entities. The selection of rules uses two criteria. The first one permits to assess the implication quality by using implication intensity measure. The second criterion verifies the generativity of the rule and then permits to reduce redundancy. Finally, the proposed method is evaluated on two benchmarks. The first contains two conceptual hierarchies containing textual documents and the second one is composed of OWL ontologies. The experimentations show that the method obtains good precision values and also permits to discover meaningful subsumptions that are not taken into account by similarity-based approaches.