Abstractions for knowledge organization of relational descriptions

Abstract

The goal of conceptual clustering is to construct a hierarchy of concepts which cluster objects based on their similarities. Knowledge organization aims at generating the set of maximally specific concepts for all possible classifications: the Generalization Space. Our research focuses on the organization of relational data represented using conceptual graphs. Unfortunately, the generalization of relational descriptions necessary to build the Generalization Space leads to a combinatorial explosion. This paper proposes to incrementally introduce the relations by using a sequence of languages that are more and more expressive. The algorithm proposed, called KIDS, is based upon an iterative reformulation of the objects descriptions. Initially represented as conceptual graphs, they are reformulated into abstract objects represented as < pairs. This representation allows us to use an efficient propositional knowledge organization algorithm. Experiments on Chinese character databases show the interest of using KIDS to build organizations of relational concepts.