Modeling Default Induction with Conceptual Structures

Our goal is to model the way people induce knowledge from rare and sparse data. This paper describes a theoretical framework for inducing knowledge from these incomplete data described with conceptual graphs. The induction engine is based on a non-supervised algorithm named default clustering which uses the concept of stereotype and the new notion of default subsumption, the latter being inspired by the default logic theory. A validation using artificial data sets and an application concerning an historic case are given at the end of the paper.

• Type: Conference
• Year: 2004
• Where: ER
• Authors: Julien Velcin, Jean-Gabriel Ganascia
• Keywords: Algorithm Named Default, Default Logic Theory, ER 2004, People Induce Knowledge