Construction Methods of Knowledge Graph and Domain-Specific LLM for Coal Mine Rockburst

Linlin Ding, Zhiyong Zhang, Yuhang Hu, Chenli Zhu, Yiming Han

Author details

Linlin Ding
Liaoning University, Shenyang, China
Zhiyong Zhang
Liaoning University, Shenyang, China
Yuhang Hu
Liaoning University, Shenyang, China
Chenli Zhu
Liaoning University, Shenyang, China
Yiming Han
Liaoning University, Shenyang, China

https://doi.org/10.15593/2712-8008/2026.1.5

Coal mine rockburst is a complex dynamic disaster induced by the accumulation and sudden release of energy during underground coal extraction. With the continuous increase in mining depth and intensity, both the frequency and severity of rockburst incidents have shown a significant upward trend, posing growing threats to mine safety, equipment integrity, and production continuity. Despite extensive research, the prevention and control of rockburst still face major challenges, including fragmented multi-source heterogeneous data, knowledge silos across systems, limited causal chain reasoning, and difficulties in timely and accurate decision-making. These issues hinder the comprehensive understanding of disaster mechanisms and reduce the effectiveness of prevention strategies. To address these limitations, this study proposes a systematic framework for constructing and enhancing a coal mine rockburst knowledge graph supported by a domain-specific large language model. The framework integrates multi-source data fusion, ontology-driven semantic modeling, and causal reasoning to establish a structured and machine-understandable representation of rockburst-related knowledge. Meanwhile, the domain-specific large language model is employed to improve knowledge extraction, enhance node semantics, support intelligent question answering, and enable dynamic updating of the knowledge graph. By combining the strengths of knowledge graphs and large language models, the proposed approach provides a unified platform for knowledge governance in the rockburst domain. It effectively improves the accessibility, interpretability, and analytical depth of rockburst-related knowledge, offering strong support for risk assessment, disaster prediction, and intelligent decision-making in coal mine safety management.

Keywords

coal mine rockburstknowledge graphdomain-specific large language modelcausal reasoningmulti-source data fusionknowledge governanceintelligent decision support

Методы построения графа знаний и предметно-ориентированной модели LLM для анализа горных ударов на угольных шахтах

Горные удары на угольных шахтах представляют собой сложную динамическую катастрофу, вызванную накоплением и внезапным высвобождением энергии во время подземной добычи угля. С непрерывным увеличением глубины и интенсивности добычи угля частота и тяжесть инцидентов, связанных с горными ударами, демонстрируют значительную тенденцию к росту, представляя растущую угрозу безопасности шахт, целостности оборудования и непрерывности добычи. Несмотря на обширные исследования, предотвращение и контроль горных ударов по-прежнему сталкиваются с серьезными проблемами, включая фрагментированные разнородные данные из множества источников, разрозненность знаний в различных системах, ограниченную причинно-следственную связь и трудности в своевременном и точном принятии решений. Эти проблемы препятствуют всестороннему пониманию механизмов катастрофы и снижают эффективность стратегий предотвращения. Для решения этих проблем в данном исследовании предлагается систематическая структура для построения и совершенствования графа знаний о горных ударах на угольных шахтах, поддерживаемого предметно-ориентированной большой языковой моделью. Предлагаемая структура объединяет слияние данных из нескольких источников, семантическое моделирование на основе онтологий и причинно-следственные рассуждения для создания структурированного и машиночитаемого представления знаний, связанных с горными ударами. Одновременно используется предметно-ориентированная LLM для улучшения извлечения знаний, повышения семантики узлов, поддержки интеллектуального ответа на вопросы и обеспечения динамического обновления графа знаний. Сочетая преимущества графов знаний и больших языковых моделей, предлагаемый подход обеспечивает единую платформу для управления знаниями в области горных ударов. Он эффективно повышает доступность, интерпретируемость и аналитическую глубину знаний, связанных с горными ударами, обеспечивая мощную поддержку оценки рисков, прогнозирования катастроф и принятия интеллектуальных решений в управлении безопасностью угольных шахт.

Ключевые слова

горные удары на угольных шахтахграф знанийпредметно-ориентированная большая языковая модельпричинно-следственная связьслияние данных из нескольких источниковуправление знаниямиинтеллектуальная поддержка принятия решений

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Article information

Published: 12 January 2026
Issue date: 12 January 2026
Issue: Vol. 26, No. 1 (2026)