Accounting for Domain Knowledge in the Construction of a Generalization Space

Our study registers in the framework of the automatic construction of classiications. We tackle an issue which has been less explored , that of the discovery of classiications. To tackle this problem we have chosen to pursue and develop the works of Mineau in the domain of the organization of knowledge bases using generalization 20]. We propose an original approach, called COING, to the discovery of classiications of structured objects represented using conceptual graphs. This approach consists in building a space of concepts which generalize the objects descriptions, called the Generalization Space, and then exploring this space so as to iteratively extract one or several conceptual classiications 2]. In this paper, we describe the method of construction of the Generalization Space that has been implemented in COING. This method is an extension of the MSG proposed by Mineau 19], which enables to account for knowledge about the types during the construction of the Generalization Space. However, we propose a formalization of the use of constraints in the construction of the Generalization Space. We present empirical results of the method proposed that show its eeency.