A Large Language Model-Driven Framework for Automated Knowledge Graph Construction for Aircraft Fault Diagnosis

As the aviation industry develops rapidly, the increasing complexity of aircraft systems demands more efficient and accurate fault diagnosis. Knowledge Graphs (KGs) enable structured fault knowledge organization but are costly to construct. Large Language Models (LLMs) offer new possibilities for automated domain KG construction with their natural language processing capabilities. This paper proposes an LLM-driven framework for aircraft fault diagnosis KG automation. It preprocesses unstructured data via hierarchical chunking to fit LLM context limits while retaining semantics. The core is a prompt-based “extract-verify-correct” iterative mechanism, which extracts entities/relations accurately and mitigates LLM hallucination and omission. Finally, knowledge vectorization and similarity calculation fuse local knowledge to eliminate redundancy, building a unified KG. The paper details the framework' s design and key technologies, providing a high-quality knowledge base for intelligent fault diagnosis and advancing aviation maintenance intelligence.