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相关实验视频

Updated: Jun 25, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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M2ixKG: 在知识图中混合更硬的负样本.

Feihu Che1, Jianhua Tao2

  • 1Department of Automation, Tsinghua University, Beijing, China.

Neural networks : the official journal of the International Neural Network Society
|May 28, 2024
PubMed
概括

本研究介绍了M2ixKG,这是一种用于知识图嵌入 (KGE) 的新混合策略. M2ixKG产生了更高质量的负样,显著提高了KGE模型的性能和稳定性.

关键词:
硬的负面是硬的负面.知识图表知识图表混合操作是混合操作.负采样采集 负采样采集

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 知识图嵌入 (KGE) 将实体和关系映射到低维向量上.
  • 有效的KGE依赖于区分正的和负的三胞胎.
  • 目前的KGE方法难以产生高质量的负样本,阻碍了模型的性能.

研究的目的:

  • 解决KGE现有的负采样策略的局限性.
  • 引入一种新的混合策略,M2ixKG,用于生成更硬的负样本.
  • 提高KGE模型的稳定性和通用化能力.

主要方法:

  • M2ixKG采用混合策略在知识图中生成负样本.
  • 它涉及在三胞胎中混合头和尾,三胞胎共享相同的关系.
  • 它还混合高得分的负样本,以创建更具挑战性的样本.

主要成果:

  • 在三个数据集上的实验证明了M2ixKG的卓越性能.
  • M2ixKG显著优于以前的负采样算法.
  • 该策略增强了实体嵌入的稳定性和概括性.

结论:

  • M2ixKG在KGE的负采样中提供了显著的进步.
  • 拟议的混合策略有效地产生更硬的负面,改善模型性能.
  • 这种方法为知识图嵌入提供了更强大和更可通用的解决方案.