Conference Agenda

This study presents a pipeline that combines Large Language Models (LLMs) and Knowledge Graphs (KGs) to automate the extraction and validation of semantic features from technical requirements in industrial engineering documents. The proposed approach focuses on identifying physical quantity names, numerical values, and units of measurement from natural-language requirements and transforming them into structured, machine-readable outputs suitable for integration with Product Lifecycle Management (PLM) systems. A LLaMA 3.1 8B model was fine-tuned on a manually annotated synthetic dataset and compared with GPT-4o as a general-purpose baseline model. The extracted information was further validated through the Quantities, Units, Dimensions and Types (QUDT) knowledge graph, which supports consistency checking between units of measurement and physical quantities. The fine-tuned model outperformed GPT-4o in complete requirement-level extraction, achieving an Exact Match (EM) score of 73.7 after knowledge graph integration. A case study involving a pressure-regulating valve further demonstrated the applicability of the complete pipeline to a different technical domain, achieving an EM score of 90 and enabling the creation of structured requirement objects in a PLM environment. The results indicate that combining task-specific LLM fine-tuning with knowledge graph-based validation can support the automated formalization of technical requirements and reduce the manual effort required to transform unstructured documents into machine-readable engineering information.