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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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概念增强的异质图形网络用于事实验证.

Zhendong Chen1, Lejian Liao2, Siu Cheung Hui3

  • 1College of Computer Science and Technology, Zhejiang Normal University, China.

Neural networks : the official journal of the International Neural Network Society
|August 16, 2025
PubMed
概括

本研究介绍了概念增强异质图形网络 (概念-HGN) 用于事实验证,通过整合多颗粒度和概念信息来提高准确性. 这种新的方法在基准数据集上取得了最先进的结果.

关键词:
常识知识是常识知识.事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证不同质的图形网络网络.多种细分信息的信息.

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科学领域:

  • 自然语言处理自然语言处理.
  • 人工智能的人工智能
  • 信息检索 信息检索

背景情况:

  • 事实验证是一个复杂的NLP任务,需要从可靠的来源获取证据.
  • 现有的方法往往忽略了多个细分信息,缺乏固有的概念理解.
  • 挑战包括聚合分散的文本线索和利用实体概念以获得准确性.

研究的目的:

  • 为改进事实验证提出一个新的概念增强异质图形网络 (概念-HGN).
  • 解决现有事实验证模型关于多细分和概念信息的局限性.
  • 提高自动化事实核查系统的准确性和稳定性.

主要方法:

  • 构建一个异质图,从多个证据句子汇总信息.
  • 在图中实现层次节点的细分化,以便有效的线索聚合.
  • 利用YAGO的内在实体概念来指导事实验证过程.

主要成果:

  • 在FEVER数据集中,概念HGN实现了80.26% (LA) 和77.68% (FS).
  • 在UKP Snopes数据集上,准确率达到65.7%,宏 F1达到61.9%.
  • 与基线模型相比,拟议的模型表现出优越的性能.

结论:

  • 概念-HGN有效地整合了多颗粒度和概念信息,用于事实验证.
  • 该模型实现了最先进的性能,超过了现有方法.
  • 这种方法为推进自动化事实核查技术提供了一个有希望的方向.