Reasoning on an ontology is presently limited to the logical one. However, in the case of inconsistent knowledge or unreliable and incomplete information, it is difficult for a system to make a decision or measure the degree of truth of a hypothesis. Several approaches have been made and focused mainly on how to represent probabilistic information in ontologies and then use them for reasoning. Although, this requires a great effort for systems already designed and based on ontological or Bayesian knowledge bases. In these cases the users must integrate the probabilistic information of Bayesian networks (BN) in ontology manually. We propose a new approach to integrate the BN (more particularly Object-Oriented Bayesian Networks (OOBn)) with OWL by providing a Jena API (Application Programming Interface). OOBNs is an extension of the standard BN using the paradigm object. Our approach (i) uses the BN knowledge base for ontological enrichment, (ii) allows the adjustment of BN structures through structural and parametric learning based on ontological knowledge bases, (iii) integrate probabilistic information into SPARQL queries. In addition, we propose a solution in which we use a selection of instances according to the needs of the user described by a set of SPARQL requests.