Une mesure de proximité et une méthode de regroupement pour l'aide à l'acquisition d'ontologies spécialisées
Abstract
In this paper, we study the problem of clustering textual units in the framework of helping an expert to build a specialized ontology. This work has been achieved in the context of a French project, called BIOTIM, handling botany corpora. %One of its goal is the acquisition of a semantic organization from textual information. Building an ontology, either automatically or semi-automatically is a difficult task. We focus on one of the main steps of that process, namely structuring the textual units occurring in the texts into classes, likely to represent concepts of the domain. The approach that we propose relies on the definition of a new non-symmetrical measure for evaluating the semantic proximity between lemma, taking into account the contexts in which they occur in the documents. Moreover, we present a non-supervised classification algorithm designed for the task at hand and that kind of data. The first experiments performed on botanical data have given relevant results.