Knowledge graph construction and visualization analysis of coal mine accident safety management research based on CiteSpace and VOSviewer

Authors

  • Zemao Yu

DOI:

https://doi.org/10.56028/aetr.8.1.914.2023

Keywords:

Coal mine safety; Scientific method of measurement; WOS; Safety management; Risk management; Research hotspot.

Abstract

Although the coal mining industry has implemented mechanization and automation, frequent coal mine accidents still occur, making coal mine safety a primary concern. To gain a comprehensive understanding of global progress in preventing coal mine accidents and managing safety, we utilized the Web of Science database (WOS) as our sample source. With the support of CiteSpace and VOSviewer software, we constructed a knowledge graph depicting publication numbers, research institutions, and keyword clustering to visually analyze coal mine accident prevention and safety management. Our research reveals that this field has undergone three stages of development: initial growth, steady progress, and rapid advancement. Particularly noteworthy is the current golden age of rapid development from 2018 to 2023 which has garnered significant attention from researchers. In terms of author collaboration networks, notable contributors with high publication rates and citation counts include Wu Qiang, Liu Quanlong, et al. Regarding institutional cooperation networks, major research institutions such as China University of Mining & Technology, Shandong University of Science & Technology, and Anhui University are actively engaged in important scientific research related to coal mining. Through keyword clustering analysis based on relevant literature review, it is evident that research on coal mine safety management primarily focuses on high-frequency keywords such as "coal and gas outburst","risk assessment" and "coal miners" By extracting the top nine emergent keywords with high-intensity levels and arranging them chronologically according to their emergence year, the evolution pattern over time was obtained for emergent words. Furthermore, it was observed that emerging keywords like "China", "subsidence" and "workers" have also gained increasing attention during this period.

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Published

2023-12-08