ERSDMM: A Standard Digitalization Modeling Method for Emergency Response Based on Knowledge Graph

Wenling Liu (), Yuexiang Yang (), Xinyu Tu and Wan Wang
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Wenling Liu: School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China
Yuexiang Yang: School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China
Xinyu Tu: School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China
Wan Wang: Sub-Institute of Public Safety Standardization, China National Institute of Standardization, Beijing 100191, China

Sustainability, 2022, vol. 14, issue 22, 1-18

Abstract: Standard digitalization is a crucial step in social and economic development and the transformation of digital technology. Standard digitalization is of great significance in the promotion of sustainable economic and social development. This paper proposes a standard digitalization modeling method for emergency response (ERSDMM) based on knowledge graph (KG). Firstly, this paper analyzes the knowledge structure of emergency response standards (ERS) and constructs a “seven-dimensional” model of ERS based on the public safety triangle theory. An ontology model of the emergency response domain is then created. Secondly, ERS and emergency scenario fine-grained knowledge are extracted. Thirdly, a standard reorganization model is constructed to meet the needs of the scenario response. Finally, the ERSDMM is applied to the GB 21734-2008, which proves that the ERSDMM is available. Taking RES as an example, this paper explores the path and practice of standard digitalization. ERSDMM solves standards-related problems, such as overlapping content, coarse knowledge granularity, incomplete coverage of elements, and difficulty in acquiring knowledge.

Keywords: knowledge graph; standard digitalization; emergency response standard; knowledge extraction; standard reorganization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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