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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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基于依赖分析和图形神经网络的实体关系提取方法.

Fupeng Wei1, Xing Liu2, Limin Pan3

  • 1School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.

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|December 30, 2025
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概括

通过整合语法分析和图形神经网络,MGRel模型改善了校园安全的实体关系提取. 这提高了准确性,处理复杂的关系,促进了交通安全知识的获取.

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在依赖性分析中,我们进行了依赖性分析.实体关系提取实体关系提取图形神经网络是一个神经网络.智能校园安全 智能校园安全三重分类是三重的分类.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 当前知识图的三元提取方法与文本中的语义模糊性和重叠关系作斗争.
  • 在校园安全数据中,实体分离和远处实体之间的弱关联降低了提取准确性和回忆.
  • 现有的技术缺乏灵活性来管理复杂的,重叠的关系,这对于校园交通安全管理至关重要.

研究的目的:

  • 引入MGRel模型,用于在校园安全环境中增强实体关系提取.
  • 解决当前方法中关于模糊性,重叠关系和实体分离的局限性.
  • 改进校园安全治理的知识获取,特别是交通安全.

主要方法:

  • 在MGRel模型中将依赖语法分析与图形神经网络 (GNN) 集成.
  • 使用双重分析机制 (全球语义依赖和语法依赖) 来捕获远距离关联.
  • 开发一个层次性的语义图卷积神经网络和一个以注意力驱动的多功能融合模块,用于精细的特征提取和分类.

主要成果:

  • 与最佳模型相比,MGRel模型在基准数据集:NYT (+1.3%),WebNLG (+0.4%) 和DuIE (+3.2%) 上显示出更好的F1分数.
  • 有效地捕获远距离的语义关联,精细地提取暗示的语义特征.
  • 通过噪声过,增强三元分类器的辨别能力.

结论:

  • 在实体关系提取方面,MGRel模型显著优于现有的技术.
  • 该模型显示了校园安全应用的巨大优势和潜在价值,特别是校园交通安全.
  • 语法分析和GNN的整合为复杂的关系提取挑战提供了强大的解决方案.