煤矿领域知识图谱构建与推理方法研究综述

罗香玉; 华颖; 王喜平; 解盘石; 伍永平

doi:10.13272/j.issn.1671-251x.2024090069

煤矿领域知识图谱构建与推理方法研究综述

罗香玉^1,,
华颖¹,
王喜平¹,
解盘石^{2, 3},
伍永平^{2, 3}

1.
西安科技大学计算机科学与技术学院，陕西西安　710600
2.
西安科技大学西部矿井开采及灾害防治教育部重点实验室，陕西西安　710054
3.
西安科技大学能源学院，陕西西安　710054

基金项目: 陕西省杰出青年科学基金项目（2023-JC-JQ-42）；陕西省教育厅青年创新团队科研计划项目（23JP098）；陕西省秦创原“科学家+工程师”队伍建设项目（2024QCY-KXJ-033）。

详细信息

作者简介:
罗香玉（1984—），女，河北宁晋人，副教授，博士，主要研究方向为智慧矿山、大数据分析和知识图谱，E-mail：luoxiangyu@xust.edu.cn

中图分类号: TD67
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出版历程
- 收稿日期: 2024-09-18
- 修回日期: 2024-11-12
- 网络出版日期: 2024-10-31
- 刊出日期: 2024-11-24

Review of construction and reasoning methods for knowledge graphs in coal mining domain

1.
College of Computer Science & Technology, Xi'an University of Science and Technology, Xi'an 710600, China
2.
Key Laboratory of Western Mine Exploitation and Hazard Prevention, Ministry of Education, Xi'an University of Science and Technology, Xi'an 710054, China
3.
College of Energy Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

摘要

摘要:
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。
- 智慧矿山 /
- 煤矿领域知识图谱 /
- 知识图谱构建 /
- 知识图谱推理 /
- 知识图谱融合 /
- 知识图谱纠错 /
- 智能决策
Abstract:
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.
- smart mine /
- knowledge graph in the coal mining domain /
- knowledge graph construction /
- knowledge graph reasoning /
- knowledge graph integration /
- knowledge graph error correction /
- intelligent decision-making

HTML全文

图 1 以知识为驱动的人工智能发展阶段

Figure 1. Evolutionary stages of knowledge-driven artificial intelligence

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图 2 基于知识图谱的人工智能系统架构

Figure 2. Architecture of artificial intelligent systems based onknowledge graphs

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图 3 知识图谱构建与推理技术关系

Figure 3. Relationship between knowledge graph construction and reasoning techniques

下载: 全尺寸图片幻灯片

表 1 煤矿领域知识图谱代表性工作技术成熟度情况

Table 1 Technological maturity of representative work of knowledge graph in coal mining domain

应用场景	代表性工作	知识建模情况	知识图谱构建方式	知识图谱使用方式	成熟度评价
煤矿灾害防治	许娜等^[29]	依据专家经验确定概念类别和关系类型	自动构建	仅以查询方式使用知识图谱	★★★☆☆
	罗香玉等^[30]	依据专家经验确定概念类别和关系类型	自动构建	仅以查询方式使用知识图谱	★★★☆☆
	刘鹏等^[31]	仅对相关概念进行分类，没有定义关系类型	自动构建	仅以查询方式使用知识图谱	★★☆☆☆
	刘永立等^[32]	概念类别和关系类型均未明确定义	人工构建	仅探讨知识图谱推理的潜在应用，未实现推理	★☆☆☆☆
	吴克介^[33]	依据专家经验确定概念类别和关系类型	自动构建	仅以查询方式使用知识图谱	★★★☆☆
煤矿机电设备维护	李哲等^[34]	依据专家经验确定概念类别和关系类型	人工和自动构建相结合	仅探讨知识图谱推理的潜在应用，未实现	★★★☆☆
	韩一博等^[35]	依据专家经验确定概念类别和关系类型	自动构建	未提及	★★☆☆☆
	冯银辉等^[36]	依据专家经验确定概念类别和关系类型	自动构建	仅以查询方式使用知识图谱	★★★☆☆

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表 2 知识图谱构建与推理技术面临的主要挑战

Table 2 Main challenges in knowledge graph construction and reasoning techniques

技术名称	所属类别	主要挑战	技术难点描述
实体识别	知识图谱构建基础技术	嵌套实体的识别	单个字可同时属于多个实体，基于字分类器的实体识别方法无法适用
关系抽取	知识图谱构建基础技术	实体重叠条件下的关系抽取	单个实体可同时属于多个三元组，基于实体对分类器的关系抽取方法无法适用
知识图谱融合	知识图谱构建完善技术	异构知识图谱间的实体对齐	基于知识图谱结构的实体嵌入表示方法无法适用
知识图谱纠错	知识图谱构建完善技术	实体间关系的一致性约束建模	一致性约束建模的完备性评价
基于图神经网络的知识图谱推理	知识图谱推理技术	实体的嵌入表示	多个图卷积层在增强图特征提取充分性的同时，会造成实体嵌入表示误差的传播
基于强化学习的知识图谱推理	知识图谱推理技术	多跳推理下的路径选择	指数级增长的搜索空间带来的计算复杂性

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