Description
This article presents a hybrid, extensional and asymmetric matching approach designed to find out relations (equivalence and subsumption) between two textual hierarchies. By using the as-sociation rule paradigm and a statistical measure, this method relies on the following idea: ``An entity A will be more specific than or equivalent to an entity B if the vocabulary used to describe A and its instances tends to be included in that of B and its instances''. This 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 for assessing the implication quality and reducing redundancy. The method is evaluated on two benchmarks. The first contains two hierarchies indexing textual documents and the second one is composed of OWL ontologies.