Question and feedback generation from ontologies has appeal for the high-quality content it can provide, and to alleviate the test setting and marking burden of teachers and quiz question setters. Existing approaches lack a formal and expressive technique, however, which limits their ability to represent and process complex and semantically rich questions, answers, and explanatory feedback patterns. They typically require post-processing to eliminate irrelevant outputs, rely on entity labels rather than represented semantics, and they often lack modularity, reducing scalability and reusability. We solve this by focusing on the content determination stage in the natural language generation pipeline, and any realiser of choice to be used afterwards. We propose a framework consisting of an OWL 2 DL-based model combined with an optimised determination algorithm to efficiently fetch relevant content from the ontology. The framework was implemented and evaluated on content selection performance with several ontologies.