PurposeLearning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.Design/methodology/approachThe ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases.
| Authors | Wu, Wenjing and Wen, Caifeng and Yuan, Qi and Chen, Qiulan and Cao, Yunzhong |
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| Year | 2025 |
| Venue | — |
| DOI | 10.1108/ecam-03-2023-0255 |
| Source Database | EBSCO (Emerald Insight) |
| Bridge-to-GNN | Category C |
| GNN Architecture | Knowledge Graph Embedding (inferred from title) |
| Graph Encoding | Knowledge Graph / Ontological Network (inferred from title) |
| AEC Task | Knowledge Graph Construction (inferred from title) |
| Cohort | Mature Applications (2024–2026) |
| Primary Metric | Precision (heuristic — verify) |
|---|---|
| Primary Value | 84.52% (heuristic — verify) |
| Reported metrics + values | F1 Score: 88.26% · Precision: 84.52% · Recall: 92.35% |
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| Quality (heuristic, 6-flag) | 0/6 · rigor: Low Code/Data: —Benchmark: —Baseline: —CV/Split: —Ablation: —Reproducible: — |
| Bibliometrically related to other seeds | This paper appeared in the Connected Papers neighbourhood of these corpus seeds: [61] 2020 · Rasmussen · BOT: Building Topology Ontology |
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Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026