一个用于危险化学品回收识别和属性分类的文本特征提取模型,嵌入域知识图中
Quan Cheng1, Shuangbao Zhang2, Lanyu Yang3
1School of Economics and Management, Fuzhou University, Fuzhou, China.
Environmental monitoring and assessment
|March 18, 2025
概括
本研究引入了一种用于识别危险化学品,改善环境保护和可持续发展的新模式. 该KG-TextRCNN模型显著提高了识别和分类化学危险的准确性.
科学领域:
- 环境科学 环境科学
- 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
背景情况:
- 工业生产和研究经常使用危险化学品,需要有效的回收和对环境健康的危害特征.
- 目前用于分析危险化学品的表查询方法效率低下,突出显示了对大数据等先进技术的需求.
研究的目的:
- 提高对危险化学品特性和风险的识别和理解.
- 开发一种自动化模型来识别危险的化学信息,减少主观错误.
主要方法:
- 将危险化学品领域知识图纳入风险识别中.
- 构建一个名为KG-TextRCNN的危险化学回收文本识别模型.
主要成果:
- KG-TextRCNN模型在文本体识别方面实现了99.30%的平均精度,回忆和F1分数超过99%.
- 使用最大聚合的分类步骤产生了平均精度,回忆和F1分数分别为85.19%,86.29%和85.27%.
- 该模型在大多数类别中表现出稳定的整体性能,高精度和召回率.
结论:
- 开发的KG-TextRCNN模型有效地识别危险化学品,比传统方法有了显著的改进.
- 知识图和深度学习的整合提高了危险化学品风险评估的准确性和可靠性.
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