During the past decades, many methods have been developed for the creation of Knowledge-Based Systems (KBS). For these methods, probabilistic networks have shown to be an important tool to work with probability-measured uncertainty. However, quality of probabilistic networks depends on a correct knowledge acquisition and modelation. KAMET is a model-based methodology designed to manage knowledge acquisition from multiple knowledge sources that leads to a graphical model that represents causal relations. Up to now, all inference methods developed for these models are rule-based, and therefore eliminate most of the probabilistic information. We present a way to combine the benefits of Bayesian networks and KAMET, and reduce their problems. To achieve this, we show a transformation that generates directed acyclic graphs, the basic structure of Bayesian networks, and conditional probability tables, from KAMET models. Thus, inference methods for probabilistic networks may be used in KAMET models.