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CPBA-CLIM:一种用于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提取模型。

CPBA-CLIM: An entity-relation extraction model for ontology-based knowledge graph construction in hazardous chemical incident management.

作者信息

Du Wanru, Wang Xiaoyin, Zhu Quan, Jing Xiaochuan, Liu Xuan

机构信息

China Aerospace Academy of Systems Science and Engineering, Beijing, China.

Aerospace Hongka Intelligent Technology (Beijing) Co., Ltd., Beijing, China.

出版信息

Sci Prog. 2024 Jan-Mar;107(1):368504241235510. doi: 10.1177/00368504241235510.

Abstract

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.

摘要

近年来,由于人为或自然因素,危险化学品事故频繁发生,造成了重大人员伤亡、财产损失和环境污染。准确挖掘从累积的事故报告中吸取的教训并构建危险化学品事故管理的知识图谱,可以帮助管理人员识别模式并分析共同属性,从而防止类似事故再次发生。本文探讨了危险化学品事故中分散的文本信息、专业词汇和数据格式等挑战。我们提出了一种名为CPBA-CLIM(基于内容-位置的注意力-交叉标签相交匹配)的新型实体关系提取模型,为构建危险化学品事故知识图谱提供准确的数据基础。基于内容-位置注意力的基于内容-位置的注意力模块,将上下文语义信息纳入来自变压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,交叉标签相交匹配策略评估了包含潜在重叠的集合中实体关系交互的合理性,减少了实体关系重叠对三元组提取准确性的影响。在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性能。在自建数据集上的定性实验将我们的模型与本体构建技术相结合,成功建立了一个用于管理危险化学品事故的知识图谱。本研究有效地提高了知识图谱构建的自动化程度和效率,从而为危险化学品安全管理提供支持和决策基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71f7/10943738/afc638035882/10.1177_00368504241235510-fig1.jpg

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