The goal of conceptual clustering is to build a set of embedded classes, which cluster objects based on their similarities. Knowledge organization aims at generating the set of most specific classes: the Generalization Space. It has applications in the field of data mining, knowledge indexation or knowledge acquisition. Efficient algorithms have been proposed for data described in éattribute, valueé pairs formalism and for taking into account domain knowledge. Our research focuses on the organization of relational knowledge represented using conceptual graphs. In order to avoid the combinatorial explosion due to the relations in the building of the Generalization Space, we progressively introduce the complexity of the relations. The KIDS algorithm is based upon an iterative data reformulation which allows us to use an efficient propositional knowledge organization algorithm. Experiments show that the KIDS algorithm builds an organization of relational concepts but remains with a complexity that grows linearly with the number of considered objects.