Bayesian networks are indispensable for determining the probability of events which are influenced by various components. Bayesian probabilities encode degrees of belief about certain events and a dynamic knowledge body is used to strengthen, update, or weaken these assumptions. The creation of Bayesian networks requires at least three challenging tasks: (i) the determination of relevant influence factors, (ii) the determination of relationships between the identified influence factors, and (iii) the calculation of the conditional probability tables for each node in the Bayesian network. Based on existing domain ontologies, we propose a method for the ontology-based generation of Bayesian networks. The ontology is used to provide the necessary knowledge about relevant influence factors, their relationships, their weights, and the scale which represents potential states of the identified influence factors. The developed method enables, based on existing ontologies, the semi-automatic generation and alternation of Bayesian networks.