CPBA-CLIM:一种实体关系提取模型,用于在危险化学事件管理中基于本体学知识图的构建
Wanru Du1,2, Xiaoyin Wang2, Quan Zhu1,2
1China Aerospace Academy of Systems Science and Engineering, Beijing, China.
Science progress
|March 15, 2024
概括
这项研究引入了一种新的模型,CPBA-CLIM,用于从危险化学事件报告中提取信息. 这有助于改进知识图的构建,以更好地管理安全和预防事故.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
- 信息科学 信息科学 信息科学
背景情况:
- 经常发生的危险化学品事故导致大量人员伤亡,财产损失和环境污染.
- 从事件报告中提取经验教训和建立知识图表可以防止未来的事件发生.
- 挑战包括分散的数据,专业的词汇和事件报告的各种格式.
研究的目的:
- 提出一种新的实体关系提取模型,CPBA-CLIM,用于在危险化学事件知识图表构建中准确的数据基础.
- 解决处理分散的文本信息,专业词汇和数据格式的挑战.
- 提高安全管理知识图构建的自动化和效率.
主要方法:
- 开发了CPBA-CLIM (基于内容位置的注意力跨标签交叉匹配) 模型.
- 整合了一个基于内容位置的注意模块,用于与主题上下文一致的动态词向量.
- 使用跨标签交叉匹配策略来减少实体关系重叠对三重提取准确性的影响.
主要成果:
- 在公开数据集上,CPBA-CLIM模型在提取重叠的三胞胎中表现出色.
- 通过使用自建数据集,定性实验成功建立了危险化学事件管理的知识图.
- 这项研究显著提高了知识图表构建的自动化和效率.
结论:
- CPBA-CLIM模型为构建危险化学事件知识图提供了准确的数据基础.
- 这种方法有效地支持对危险化学品安全管理的决策.
- 该研究推进了用于事件管理的自动化知识图表构建.
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