Uncertain reasoning in AI concerns various forms of "probabilistic" as opposed to logical inference. Our work in the area has focused on theory refinement for knowledge bases using uncertainty such as certainty-factor rule bases and Bayesian networks. Rather than building such knowledge-bases completely manually or learning them form scratch, our work focuses on using data to revise imperfect expert-supplied initial KB's.