Conceptual schema clustering allows a multilevel abstraction of the same reality. This paper addresses the development of such technique for entity-relationship and object models. We provide an automatization of conceptual schema clustering leading to a unification of past approaches. Automatization is achieved through the definition of semantic distances between concepts and the use of a clustering algorithm. For entity-relationship clustering, we define three different distances (visual, hierarchical and cohesive) depending on the semantic richness. Object clustering is based on the structural, semantic and communication characteristics of concepts. Our approach was implemented and applied to a great number of examples, leading to very satisfactory results.
COMYN-WATTIAU, I. and AKOKA, J. (1994). Classification automatique de schémas conceptuels : une approche unifiée. ESSEC Business School.