Information Extraction of Aviation Accident Causation Knowledge Graph: An LLM-Based Approach

• DOI: 10.3390/electronics13193936
• Publication Date: 2024-10-07T11:30:18Z
• Authors: Lu Chen; Jihui Xu; Tianyu Wu; Jie Liu
• Keywords: Causation; Aviation accident; Accident (philosophy); General aviation

Summarizing the causation of aviation accidents is conducive to enhancing safety. The knowledge graph accident causation, constructed based on reports, can assist in analyzing causes accidents. With continuous development artificial intelligence technology, leveraging large language models for information extraction and construction has demonstrated significant advantages. This paper proposes an method Claude-prompt, which relies large-scale pre-trained model Claude 3.5. Through prompt engineering, combined with a few-shot learning strategy self-judgment mechanism, this achieves automatic accident-cause entities their relationships. Experimental results indicate that approach effectively improves accuracy extraction, overcoming limitations traditional methods terms efficiency processing complex texts. It provides strong support subsequently constructing structured conducting analysis