English(EN) FlowNeg: GFlowNet-Guided Diverse Hard Negative Sampling for Knowledge Graph Embedding

FlowNeg方法通过多样化的负采样增强知识图谱嵌入

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-24 21:38

研究人员开发了FlowNeg，一种用于知识图谱嵌入（KGE）模型生成多样化且信息丰富的负样本的新颖方法。该方法利用上下文条件分层生成流网络来分摊与奖励成比例的采样，根据模型基础硬度和避免与保留正样本冲突的结构化分数相结合的奖励来选择实体类型，然后选择特定实体。跨不同架构和基准的实验表明，FlowNeg在平均MRR方面始终优于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

影响通过提高负采样的质量和多样性来增强知识图谱嵌入模型，有望带来更准确、更鲁棒的KGE系统。

排序理由该条目描述了在研究论文中提出的一种用于知识图谱嵌入的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-08-24 21:38

FlowNeg：GFlowNet引导的多样化硬负采样用于知识图谱嵌入

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 concentrate on few entities and collide more wit…

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FlowNeg：GFlowNet引导的多样化硬负采样用于知识图谱嵌入

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