Scholarlink

<p>Timely and accurate decision-making is critical during gas tunnel emergencies, yet relevant knowledge is typically fragmented across unstructured reports, manuals, and case records, hindering rapid access. To address this challenge, a knowledge recommendation approach was proposed that synergistically combines knowledge graph (KG) and large language model (LLM). This method first constructs a domain-specific KG from heterogeneous emergency documents using prompt-optimized LLMs. During inference stage, it retrieves the most contextually relevant subgraph via semantic similarity-based vector search and re-ranks candidates to enhance precision. This retrieved knowledge dynamically augments the LLM, grounding its responses in verified domain facts and reducing hallucinations. Evaluations show that approach substantially outperforms both standalone LLMs and alternative architectures in accuracy, completeness, and coherence. The method proposed in the study improves the speed and quality of emergency response for gas tunnels, offering a structured and interpretable approach to knowledge-driven decision support in this specific high-risk context.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Knowledge graph–large language model fusion approach for emergency knowledge recommendation in gas tunnels

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月28日

查看全文
  • Na Xu,
  • Xi Chen,
  • Jinpei Luo,
  • Fenghua An,
  • Liang Wang,
  • Xinyu Li

摘要

Timely and accurate decision-making is critical during gas tunnel emergencies, yet relevant knowledge is typically fragmented across unstructured reports, manuals, and case records, hindering rapid access. To address this challenge, a knowledge recommendation approach was proposed that synergistically combines knowledge graph (KG) and large language model (LLM). This method first constructs a domain-specific KG from heterogeneous emergency documents using prompt-optimized LLMs. During inference stage, it retrieves the most contextually relevant subgraph via semantic similarity-based vector search and re-ranks candidates to enhance precision. This retrieved knowledge dynamically augments the LLM, grounding its responses in verified domain facts and reducing hallucinations. Evaluations show that approach substantially outperforms both standalone LLMs and alternative architectures in accuracy, completeness, and coherence. The method proposed in the study improves the speed and quality of emergency response for gas tunnels, offering a structured and interpretable approach to knowledge-driven decision support in this specific high-risk context.