In recent years, hazardous chemical incidents have occurred frequently, resulting in significant human casualties, property damage, and environmental pollution due to human or natural factors. Accurately mining the lessons learned from accumulating incident reports and constructing the knowledge graph for hazardous chemical incident management can assist managers in identifying patterns and analyzing common attributes, thereby preventing the recurrence of similar incidents. This article addresses the challenges of dispersed textual information, specialized vocabulary, and data formats in hazardous chemical incidents. We propose a novel entity-relation extraction model called CPBA-CLIM (content-position-based attention-cross-label intersect matching) to provide an accurate data foundation for constructing the hazardous chemical incident knowledge graph. The content-position-based attention module, based on content-position attention, incorporates contextual semantic information into the combined encoding of bidirectional encoder representations from the transformer's content and position to obtain dynamic word vectors that align with the thematic context of the text. Additionally, the cross-label intersect matching strategy evaluates the rationality of entity–relation interactions in sets containing potential overlaps, reducing the impact of entity–relation overlap on triplet extraction accuracy. Comparative experimental results on public datasets demonstrate the model's outstanding performance in overlapping triplets. Qualitative experiments on a self-constructed dataset integrate our model with ontology construction techniques, successfully establishing a knowledge graph for managing hazardous chemical incidents. This research effectively enhances the degree of automation and efficiency in knowledge graph construction, thus offering support and decision-making foundations for hazardous chemical safety management.

中文翻译：

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CPBA-CLIM：危险化学品事故管理中基于本体的知识图谱构建的实体关系抽取模型

近年来，危险化学品事故频发，因人为或自然因素造成重大人员伤亡、财产损失和环境污染。准确挖掘事故报告积累的经验教训，构建危化品事故管理知识图谱，可以帮助管理人员识别模式、分析共性，从而防止类似事件再次发生。本文解决了危险化学品事件中分散的文本信息、专业词汇和数据格式的挑战。我们提出了一种新颖的实体关系提取模型CPBA-CLIM（基于内容位置的注意力交叉标签相交匹配），为构建危险化学品事件知识图谱提供准确的数据基础。基于内容位置的注意力模块基于内容位置注意力，将上下文语义信息合并到来自转换器内容和位置的双向编码器表示的组合编码中，以获得与文本主题上下文对齐的动态词向量。此外，跨标签相交匹配策略评估包含潜在重叠的集合中实体-关系交互的合理性，减少实体-关系重叠对三元组提取准确性的影响。公共数据集上的对比实验结果证明了该模型在重叠三元组方面的出色表现。在自建数据集上进行的定性实验将我们的模型与本体构建技术相结合，成功建立了危险化学品事件管理的知识图谱。该研究有效提升了知识图谱构建的自动化程度和效率，为危化品安全管理提供支撑和决策基础。