将煤矿领域来自不同数据源的知识进行抽取，形成知识网络，借助推理技术可辅助煤矿设备故障诊断、安全风险实时预警与处置、灾害事故原因分析、应急救援方案生成及生产组织和运营管理决策支持，从而推进智慧矿山建设。梳理了知识图谱尤其是煤矿领域知识图谱的研究现状，介绍了以知识为驱动的人工智能发展历程、基于知识图谱的人工智能系统架构、知识图谱的主要类型和代表性工作，剖析了煤矿领域已有知识图谱的知识建模情况、知识图谱构建方式、知识图谱使用方式和成熟度。从实体识别、关系抽取、知识图谱融合与纠错、知识图谱推理等方面，对煤矿领域知识图谱构建与推理技术面临的挑战进行了分析，指出针对上述挑战，需研究基于跨度的实体识别方法、基于多堆叠分类器的关系抽取方法、实体的嵌入表示方法、实体间关系的一致性约束建模方法；煤矿领域知识图谱推理技术的研究需以应用为驱动，与业务场景密切结合；煤矿领域存在大量图像、视频等多模态数据，未来可构建煤矿领域多模态知识图谱，还可融入时间信息构建煤矿领域时序知识图谱。

Knowledge from diverse data sources in the coal mining domain is extracted to construct a knowledge network. Leveraging reasoning technologies, this network supports equipment fault diagnosis, real-time safety risk warnings and responses, disaster cause analysis, emergency rescue planning, production organization, and operational decision-making, thereby advancing intelligent mining. This paper reviews the research progress on knowledge graphs, with a focus on their applications in coal mining. It discusses the evolution of knowledge-driven artificial intelligence, the architecture of AI systems based on knowledge graphs, primary types of knowledge graphs, and representative studies. The paper examines knowledge modeling, construction, utilization, and maturity of existing knowledge graphs in the coal mining domain. Key challenges in knowledge graph construction and reasoning, spanning entity recognition, relation extraction, graph fusion and error correction, and reasoning, are analyzed. To address these challenges, proposed solutions include span-based entity recognition methods, multi-stack classifier-based relation extraction, entity embedding techniques, and consistency constraint modeling for entity relationships. Research on reasoning techniques should remain application-driven and tightly integrated with business scenarios. Given the abundance of multimodal data such as images and videos in the coal mining field, future efforts could focus on constructing multimodal and temporal knowledge graphs by incorporating time information.