Integrating machine learning and a large language model to construct a domain knowledge graph for reducing the risk of fall-from-height accidents

Abstract

Fall-from-height (FFH) accidents remain a major source of workplace injuries and fatalities. Fall protection systems (FPS) are critical for preventing falls in the work-at-height (WAH) environment. However, challenges in designing and selecting effective FPS persist across various industries, and existing tools often lack practical references. This study aims to develop an FFH-specific knowledge graph (FFH-KG) to support FPS design. By structuring accident data, the FFH-KG provides empirical insights to help designers improve FPS frameworks, aiding safety planning and decision-making. It serves as a decision support system for FPS designers and safety professionals, guiding the selection and design of appropriate protection solutions for diverse WAH scenarios. The FFH-KG was constructed using a hybrid natural language processing approach, combining manual extraction, entity recognition, text segmentation, and rule-based relation extraction. It was grounded in a schema layer (i.e., ontology) established by experts. A text-mining approach, integrating machine learning with a large language model, facilitated the categorization of fall types, refinement of WAH scenarios, and identification of fall causes, enhancing the content and applicability of knowledge graph. A total of 2,200 entities and 4,820 relationships were created based on fall protection equipment standard documents and fall-from-height accident investigation reports, forming a foundation for developing countermeasures. The retrieval performance of FFH-KG was validated through three case studies. This research has also made significant progress in intelligent safety engineering and management across industries.