Integrating machine learning and a large language model to construct a domain knowledge graph for reducing the risk of fall-from-height accidents
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View abstract on PubMed
Summary
This summary is machine-generated.A new knowledge graph for fall-from-height accidents aids in designing better fall protection systems (FPS). This system structures accident data to improve safety planning and decision-making for work-at-height scenarios.
Area Of Science
- Occupational Safety and Health
- Artificial Intelligence in Engineering
- Knowledge Management Systems
Background
- Fall-from-height (FFH) accidents are a leading cause of workplace injuries and fatalities.
- Effective fall protection systems (FPS) are crucial for work-at-height (WAH) safety.
- Current FPS design and selection face challenges due to a lack of practical, structured data.
Purpose Of The Study
- To develop a specialized knowledge graph (FFH-KG) for fall-from-height accidents.
- To support the design and selection of effective fall protection systems (FPS).
- To provide empirical insights for improving safety planning and decision-making in WAH environments.
Main Methods
- A hybrid natural language processing approach combining manual extraction, entity recognition, text segmentation, and rule-based relation extraction.
- Development of an expert-defined ontology (schema layer) for grounding the knowledge graph.
- Integration of machine learning and large language models for text mining, categorizing fall types, refining WAH scenarios, and identifying fall causes.
Main Results
- Construction of a comprehensive FFH-KG with 2,200 entities and 4,820 relationships.
- The FFH-KG is built upon fall protection equipment standards and accident investigation reports.
- Validation of FFH-KG retrieval performance through three case studies, demonstrating its practical applicability.
Conclusions
- The FFH-KG serves as a valuable decision support system for FPS designers and safety professionals.
- It enhances the development of countermeasures by providing structured, empirical insights into FFH accidents.
- The research advances intelligent safety engineering and management across industries for WAH safety.
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