Aide à la conception de méthodes de classification pour la construction d'ontologies : l'atelier Mo'K

This paper describes Mo’K, a configurable workbench that supports the development of conceptual clustering methods for ontology building. Mo’K is intended to assist ontology developers in the exploratory process of defining the most suitable learning methods for a given task. To do so, it provides facilities for evaluation, comparison and elaboration of conceptual clustering methods. Also, the model underlying Mo’K permits a fine-grained definition of similarity measures and class construction operators, easing the tasks of method instantiation and configuration. This paper presents some experimental results that illustrate the suitability of the model to help characterize and assess the performance of different methods that learn semantic classes from parsed corpora