Default clustering with conceptual structures

Abstract

This paper describes a theoretical framework for inducing knowledge from incomplete data sets. The general framework can be used with any formalism based on a lattice structure. It is illustrated within two formalisms: the attribute-value formalism and Sowa's conceptual graphs. The induction engine is based on a non-supervised algorithm called default clustering which uses the concept of stereotype and the new notion of default subsumption, inspired by the default logic theory. A validation using artificial data sets and an application concerning the extraction of stereotypes from newspaper articles are given at the end of the paper. © Springer-Verlag Berlin Heidelberg 2007.