Building Trust in the Skies: A Knowledge-Grounded LLM-based Framework for Aviation Safety

2025-12-27 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

A novel, end-to-end framework integrates Large Language Models (LLMs) and Knowledge Graphs (KGs) to enhance trustworthiness in aviation safety decision-making. This system addresses LLM limitations like factual inaccuracies and hallucination in safety-critical environments. The framework features a dual-phase pipeline: first, LLMs automate the construction and dynamic updating of an Aviation Safety Knowledge Graph (ASKG) from multimodal sources, including NTSB accident reports and FAA registration databases. Second, this curated KG is leveraged within a Retrieval-Augmented Generation (RAG) architecture to ground, validate, and explain LLM-generated responses. The implemented prototype, utilizing technologies like LangChain, spaCy, SentenceTransformers, FAISS, Neo4j, and Llama-3, demonstrates improved accuracy and traceability over LLM-only approaches, supporting complex querying and mitigating hallucination for verifiable safety insights.

Key takeaway

For AI Architects and NLP Engineers developing safety-critical systems, integrating LLMs with dynamically updated Knowledge Graphs is crucial. Your systems must move beyond traditional RAG to a closed-loop framework where LLMs build and maintain KGs, which then rigorously ground and validate real-time LLM outputs. This approach ensures traceability, mitigates hallucination, and meets stringent regulatory compliance requirements for auditability in domains like aviation.

Key insights

Combining LLMs with dynamically updated Knowledge Graphs enhances trustworthiness and verifiability in safety-critical aviation analytics.

Principles

• LLMs require grounding for safety-critical applications.
• KGs provide structured, auditable domain knowledge.
• Hybrid LLM-KG systems offer verifiable, context-aware insights.

Method

The proposed method uses a dual-phase pipeline: LLMs construct and update an Aviation Safety Knowledge Graph (ASKG), which then grounds and validates LLM-generated responses via a RAG architecture for trustworthy aviation safety analytics.

In practice

• Automate KG construction from diverse aviation data.
• Use GraphRAG for context-aware, verifiable LLM outputs.
• Implement human-in-the-loop for query refinement.

Topics

• Aviation Safety
• Large Language Models
• Knowledge Graphs
• Retrieval-Augmented Generation
• Cypher Query Language

Code references

• dumindus/aviation-safety-kg-rag
• langchain-ai/langgraph
• langchain-ai/langchain
• facebookresearch/faiss

Best for: AI Architect, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.