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[23] Knowledge Support for Emergency Response During Construction Safety Accidents

EBSCO – Screened Construction Safety & Knowledge Management 2025

Paper Description extracted from PDF · abstract · 132 words

Emergency response to construction safety accidents is the focus of this study. Despite the abundance of data and materials available for emergency response in construction safety, the unstructured nature of the knowledge and the disordered state of storage have limited the timely application of this knowledge in decision-making for emergency response. In this study, scenario-response theory, natural language processing, and deep learning technologies were employed to construct a domain knowledge graph for emergency response in the field of safety accidents. First, based on scenario-response theory and domain-specific materials, four categories of scenario domains and 14 types of scenario elements were identified. Second, according to the mapping relationships between scenario elements and emergency response knowledge, 14 entity types and 10 relationship types were determined, thereby forming the knowledge structure pattern of this field.

AuthorsTong, Han and Li, Xinyu and Shi, An and Xu, Na and Guo, Jin
Year2025
VenueApplied Sciences (2076-3417)
DOI10.3390/app152111760
Source DatabaseEBSCO (Applied Science & Technology)
Bridge-to-GNNCategory C
GNN ArchitectureNot yet extracted from PDF
Graph EncodingNot yet extracted from PDF
AEC TaskSafety Management (inferred from title)
CohortMature Applications (2024–2026)

Extracted Methodology heuristic — verify before citing

Dataset Size7143 instances (heuristic — verify)
Implementation FrameworkPyTorch, Neo4j (heuristic — verify)
Primary MetricAccuracy (heuristic — verify)
Quality Assessment2/6 · rigor: Low ·
Benchmark: ✓Baseline: ✓ (heuristic — verify)

Detected Technical Stack auto-detected from PDF · regex catalogue match

GNN architectures detectedKnowledge Graph Embedding (TransE/RotatE/DistMult)
Frameworks / librariesPyTorch · Neo4j / Cypher
Benchmark datasets referencedPubMed
Dataset stats found7143 instances
Quality (heuristic, 6-flag)2/6 · rigor: Low
Code/Data: —Benchmark: ✓Baseline: ✓CV/Split: —Ablation: —Reproducible: —

Lineage Families

safety-km

Enabled By / Precursors

[64]

Extended By

[52]

Infrastructure Dependencies

PyTorch (detected from PDF) Neo4j / Cypher (detected from PDF)

Research Frontier Tags

safety-km frontier-2025+

Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026