In the last decade, the interest and effort towards the use of ontology-based solutions for knowledge management has significantly increased. Ontologies have been used in manufacturing to provide a formal representation of the domain knowledge in a way that is machine-understandable. However, despite the ability to formally represent the elements of a domain and their relations, ontologies themselves do not provide any kind of simulation and systems behaviour analysis capabilities.Manufacturing system knowledge may be translated into specific executable models by exploiting experience and human logical deduction. This can be also achieved using ontologies and semantic reasoning.The framework presented in this work, therefore, aims to explore a W3C standard for inference rules, such as Semantic Web Rule Language (SWRL), and OWL ontology models to transform elements of a Knowledge-Base (KB) into Petri Net (PN) primitives. The combination of semantics and mathematical modelling techniques applied to the analysis of a simple automated assembly station highlights the existence of modelling patterns and the effectiveness of inference rules to automatically instantiate PN-based manufacturing system models.As results, the inference rules-driven instantiation of a semantically enriched PN model has two positive consequences: (i) the axioms upon which the manufacturing system ontology is built are easy-to-reuse; (ii) the semantics-based bridging of the analysed domains shows the possibility of further enriching the KB with both qualitative and quantitative assessment capabilities.