This paper addresses the challenge of dispersed accident-related information on construction sites, which hinders consensus among employers, workers, supervisors, and society. A robust NLP-based framework is presented to analyze and structure accident-related textual data into a comprehensive knowledge base that reveals accident patterns and risk information. Accident scenarios, including frequency and severity scores, are structured into a graph database through knowledge modeling, establishing an ontology to elucidate keyword relationships. Network analysis identifies accident patterns, quantifies scenario likelihood and severity, and predicts criticality, forming an accident hazard ontology. This vectorized ontology supports accident tracking, prediction, and learning with potential applications. The framework ensures reliable data integration, real-time hazard assessment, and proactive safety measures.

中文翻译：

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基于图的智能事故危险本体，使用自然语言处理进行跟踪、预测和学习


本文解决了建筑工地事故相关信息分散的挑战，该挑战阻碍了雇主、工人、监理和社会之间的共识。提出了一个基于 NLP 的强大框架，用于分析与事故相关的文本数据并将其构建成一个全面的知识库，以揭示事故模式和风险信息。通过知识建模将事故场景（包括频率和严重性评分）构建到图形数据库中，从而建立本体来阐明关键字关系。网络分析识别事故模式，量化情景可能性和严重性，并预测严重程度，形成事故危险本体。这种矢量化本体支持事故跟踪、预测和学习以及潜在的应用程序。该框架可确保可靠的数据集成、实时危险评估和主动安全措施。