Probabilistic Graphical Models (PGMs) are powerful tools for representing and reasoning under uncertainty. Although useful in several domains, PGMs suffer from their building phase known to be mostly an NP-hard problem which can limit in some extent their application, especially in real world applications. Ontologies, from their side, provide a body of structured knowledge characterized by its semantic richness. This paper proposes to harness ontologies representation capabilities in order to enrich the process of PGMs building. We are in particular interested in object oriented Bayesian networks (OOBNs) which are an extension of standard Bayesian networks (BNs) using the object paradigm. We show how the semantical richness of on-tologies might be a potential solution to address the challenging field of structural learning of OOBNs while minimizing experts involvement which is not always obvious to obtain. More precisely, we propose to set up a set of mapping rules allowing us to generate a prior OOBN structure by morphing an ontology related to the problem under study to be used as a starting point to the global OOBN building algorithm.