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FlowNeg 方法通过多样化负采样增强知识图谱嵌入

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 FlowNeg，一种用于在知识图谱嵌入 (KGE) 模型中生成多样化且信息丰富的负样本的新方法。该方法利用上下文条件分层生成流网络来选择既是困难负样本又避免与正例冲突的实体。跨不同架构和基准的实验表明，FlowNeg 在平均倒数排名 (MRR) 方面优于 EMU 和 IF-NS 等现有方法，证明了其在改进 KGE 模型学习方面的有效性。 AI

影响 通过改进负采样策略，提高了知识图谱嵌入模型的学习效率和准确性。

排序理由 该集群包含一篇详细介绍知识图谱嵌入新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

FlowNeg 方法通过多样化负采样增强知识图谱嵌入

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Tool
该集群包含一篇详细介绍知识图谱嵌入新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ibne Farabi Shihab, Naoshin Anzum Hridi, Joyanta Jyoti Mondal ·

    FlowNeg: GFlowNet引导的多样化硬负采样用于知识图谱嵌入

    arXiv:2608.23849v1 Announce Type: new Abstract: Negative sampling determines whether a knowledge graph embedding (KGE) model learns from informative counterexamples or wastes updates on implausible corruptions. Uniform negatives are diverse but easy, whereas hard-negative miners …

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