Reasoning in the Description Logic BEL using Bayesian Networks

Abstract

We study the problem of reasoning in the probabilistic Description Logic ℬℲ. Using a novel structure, we show that probabilistic reasoning in this logic can be reduced in polynomial time to standard inferences over a Bayesian network. This reduction provides tight complexity bounds for probabilistic reasoning in ℬℲ.