Bayesian networks are commonly used for determining the probability of events that are influenced by various variables. 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 variables (nodes), (ii) the determination of relationships between the identified variables (links), and (iii) the calculation of the conditional probability tables (CPTs) for each node in the Bayesian network. Based on existing domain ontologies, we propose a method for the ontology-based construction of Bayesian networks. The method supports (i) the construction of the graphical Bayesian network structure (nodes and links), (ii) the construction of CPTs that preserve semantic constraints of the ontology, and (iii) the incorporation of already existing knowledge facts (findings). The developed method enables the efficient construction and modification of Bayesian networks based on existing ontologies.