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This paper addresses issues in constructing a Bayesian network domain model for diagnostic purposes from expert knowledge. Diagnostic systems rely on suitable models of the domain, which describe causal relationships between problem classes and observed symptoms. Typically these models are obtained by analyzing process data or by interviewing domain experts. The domain models are usually built in the forward direction, i.e. by using the expert provided probabilities of symptoms given individual causes and neglecting the information in the backward direction, i.e. the knowledge about probabilities of problems given individual symptoms. In this paper we introduce a novel approach for the structured generation of a model that incorporates as closely as possible that subset of the unstructured multifaceted and possibly conflicting probabilistic information provided by the experts that they feel most confident in estimating.