Constructing a Coal Mine Safety Knowledge Graph to Promote the Association and Reuse of Risk Management Empirical Knowledge

Jiangshi Zhang, Yongtun Li (), Jingru Wu, Xiaofeng Ren, Yaona Wang, Hongfu Jia and Mengyu Xie
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Jiangshi Zhang: School of Emergency Management & Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Yongtun Li: School of Emergency Management & Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Jingru Wu: School of Emergency Management & Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Xiaofeng Ren: School of Safety Science, Tsinghua University, Beijing 100084, China
Yaona Wang: School of Emergency Management & Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Hongfu Jia: School of Emergency Management & Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Mengyu Xie: School of Emergency Management & Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China

Sustainability, 2024, vol. 16, issue 20, 1-16

Abstract: Coal mining production processes are complex and prone to frequent accidents. With the continuous improvement of safety management systems in China’s coal mining industry, a vast amount of coal mine safety experience knowledge (CMSEK) has been accumulated, originating from on site operations. This knowledge has been recorded and stored in paper or electronic documents but it remains unconnected, and the increasing volume of documents further complicates the reuse and sharing of this knowledge. In the era of large models and digitalization, this knowledge has yet to be fully developed and utilized. To address these issues, a risk management checklist was derived from coal mining site data. By integrating intelligent algorithm models and the coal industry knowledge engineering design, a coal mine safety experience knowledge graph (CMSEKG) was developed to enhance the efficiency of utilizing coal mine safety experience knowledge. Specifically, we creatively developed a coal mine safety experience knowledge representation framework, capable of representing coal mine risk inspection records from different sources and of various types. Furthermore, we proposed a deep learning-based coal mine safety entity recognition model (CMSNER), which can effectively extract coal mine safety experience knowledge from text. Finally, the CMSEKG was stored using the Neo4j graph database, and a knowledge graph was constructed using selected case information as examples. The CMSEKG effectively integrates fragmented safety management experience and professional knowledge, promoting knowledge services and intelligent applications in coal mining operations, thereby providing knowledge support for the prevention and management of coal mine risks.

Keywords: coal mine risk management; coal mine safety experience knowledge; knowledge graph; deep learning model; named entity recognition (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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