Using background knowledge as a bias to control the rule discovery process

Abstract

This paper investigates a way of using background knowledge in the rule discovery process. This technique is based on Generalization Distribution Table (GDT for short), in which the probabilistic relationships between concepts and instances over discrete domains are represented. We describe how to use background knowledge as a bias to adjust the prior distribution so that the better knowledge can be discovered.