Transforming informal stakeholder descriptions into clear and actionable software requirements is a critical challenge in requirements engineering. Existing Artificial Intelligence (AI)- based tools can process natural language but often overlook alignment with underlying business processes and coverage of all relevant scenarios. This paper proposes a hybrid neuralsymbolic framework, referred to as Process-Aware Requirement Generation (PARG), which integrates transformer-based process recognition with ontology-guided requirement generation. PARG extracts key process elements, such as actors, actions, conditions, and outcomes from user stories, maps them to domain-specific processes, and instantiates structured requirement statements using predefined templates. The framework further validates consistency and evaluates coverage to ensure completeness and traceability. By combining language understanding with structured process knowledge, PARG reduces manual effort, supports improved traceability, and produces requirements that are coherent, process-aware, and aligned with real-world workflows.