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.
| Authors | Tong, Han and Li, Xinyu and Shi, An and Xu, Na and Guo, Jin |
|---|---|
| Year | 2025 |
| Venue | Applied Sciences (2076-3417) |
| DOI | 10.3390/app152111760 |
| Source Database | EBSCO (Applied Science & Technology) |
| Bridge-to-GNN | Category C |
| GNN Architecture | Not yet extracted from PDF |
| Graph Encoding | Not yet extracted from PDF |
| AEC Task | Safety Management (inferred from title) |
| Cohort | Mature Applications (2024–2026) |
| Dataset Size | 7143 instances (heuristic — verify) |
|---|---|
| Implementation Framework | PyTorch, Neo4j (heuristic — verify) |
| Primary Metric | Accuracy (heuristic — verify) |
| Quality Assessment | 2/6 · rigor: Low · Benchmark: ✓Baseline: ✓ (heuristic — verify) |
| GNN architectures detected | Knowledge Graph Embedding (TransE/RotatE/DistMult) |
|---|---|
| Frameworks / libraries | PyTorch · Neo4j / Cypher |
| Benchmark datasets referenced | PubMed |
| Dataset stats found | 7143 instances |
| Quality (heuristic, 6-flag) | 2/6 · rigor: Low Code/Data: —Benchmark: ✓Baseline: ✓CV/Split: —Ablation: —Reproducible: — |
Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026