Thales aims to develop a virtual assistant to support pilots during flights, with a central component being the knowledge base. This knowledge base is built using a knowledge representation (KR) and reasoning system, encompassing various knowledge types including static and dynamic. However, existing KR systems present some limitations which include expressiveness and reasoning performance. The problem of expressiveness can be addressed by integrating two distinct KR concepts: Rules and Ontologies. Ontologies offers a framework for formalizing concepts, properties, and relationships, whereas rules express knowledge through IF-Then constructs. Integrating these approaches enriches knowledge representation and reasoning systems in many ways and helps to achieve completeness. However, this integration poses challenges, such as the difficulty of aligning their semantics and addressing issues of decidability. This thesis focuses on defining a methodology to combine rules and ontologies to overcome these challenges and build an optimized reasoner to execute the reasoning tasks of the virtual assistant ensuring good performance.