At the intersection of data mining and knowledge management, we shall hereafter present an extensional and asymmetric matching approach designed to find semantic relations (equivalence and subsumption) between two textual taxonomies or ontologies. This approach relies on the idea that an entity
will be more specific than or equivalent to an entity B if the vocabulary (i.e. terms and data) used to describe
and its instances tends to be included in that of
and its instances. In order to evaluate such implicative tendencies, this approach makes use of association rule model and Interestingness Measures (IMs) developed in this context. More precisely, we focus on experimental evaluations of IMs for matching ontologies. A set of IMs has been selected according to criteria related to measure properties and semantics. We have performed two experiments on a benchmark composed of two textual taxonomies and a set of reference matching relations between the concepts of the two structures. The first test concerns a comparison of matching accuracy with each of the selected measures. In the second experiment, we compare how each IM evaluates reference relations by studying their value distributions. Results show that the implication intensity delivers the best results.
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