Combining expert knowledge and user explanation with automated reasoning in domains with uncertain information poses significant challenges in terms of representation and reasoning mechanisms. In particular, reasoning structures understandable and usable by humans are often different from the ones used for automated reasoning and data mining systems. Rules with certainty factors represent one possible way to express domain knowledge and build expert system that can deal with uncertainty. Although convenient to humans, this approach has limitations in accurately modeling the domain. Alternatively, a Bayesian Network allows accurate modeling of a domain and automated reasoning but its inference is less intuitive to humans. In this paper, we propose a method to combine these two frameworks to build Bayesian Networks from rules and derive user understandable explanations in terms of these rules. Expert specified rules are augmented with importance parameters for antecedents and are used to derive probabilistic bounds for the Bayesian Network's conditional probability table. The partial structure constructed from the rules is fully learned from the data. The paper also discusses methods for using the rules to provide user understandable explanations, identify incorrect rules, suggest new rules and perform incremental learning.