LLM-Based Formalization of Engineering Requirements into Ontology-Constrained Knowledge Graphs

Requirements engineering plays a central role in mechanical design, yet technical requirements remain predominantly expressed in natural language, limiting traceability, validation, and computational reasoning. This work presents an ontology-constrained pipeline for transforming natural-language engineering requirements into Industrial Ontologies Foundry (IOF)-grounded knowledge graphs enriched with QUDT-based quantitative semantics. The pipeline decomposes text blocks into individual prescriptive clauses, extracts structural slots and constraint atoms through a typed intermediate representation, normalizes quantitative expressions via QUDT unit and quantity-kind grounding, and instantiates IOF-compliant OWL ABox graphs. The transformation is implemented as a hybrid neuro-symbolic workflow that combines Large Language Models (LLMs) with typed intermediate representations, rule-based post-processing, and description-logic reasoning. Evaluation on a Formula SAE (FSAE) rules corpus, intentionally selected to stress quantitative constraint handling, shows good structural reliability in the evaluated setting. Slot-level extraction achieved a macro accuracy of 94.50%, while quantitative constraint identification reached 97.64% precision and 96.88% recall. Normalization coverage was 98.78%, with residual errors primarily attributable to quantity-kind disambiguation. At the graph level, 93.33% of grounded artifacts passed all ontology-conformance checks, with residual violations concentrated in requirement–specification linkage and specification typing rather than in quantitative-value modeling or systematic misuse of the IOF backbone. These results indicate that ontology-constrained LLM pipelines can support the formalization of engineering requirements into semantically explicit graph representations that are suitable for downstream querying, validation, and analysis.