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新的正则化方法解决了知识图谱嵌入中的候选集干扰问题

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种名为Matched Excess-Outranker Regularization (MEOR) 的新方法，以解决持续知识图谱嵌入中的候选集干扰问题。这种干扰发生在当新实体的引入改变现有答案的排名时，即使它们的得分保持稳定。MEOR引入了一个主机级目标，仅当新来者的压力超过结构匹配的旧参考时才对其进行惩罚，从而保留了合法新实体的学习信号。实验表明，MEOR提高了历史当前宇宙平均倒数排名 (MRR)，并减少了候选集干扰，其性能优于重放和持久校准等现有方法。 AI

影响 这项研究通过更好地处理动态数据，有望提高依赖知识图谱的信息检索系统的准确性和相关性。

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学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的正则化方法解决了知识图谱嵌入中的候选集干扰问题

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该集群包含一篇详细介绍知识图谱嵌入新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hao Ren, Junbin Gao, Jiaojiao Jiang ·

    用于持续知识图谱嵌入中候选集干扰的匹配超排序正则化

    arXiv:2608.24273v1 Announce Type: new Abstract: Continual knowledge graph embedding updates entity and relation representations as a graph grows. Existing methods primarily address catastrophic forgetting, but entity admission also changes the candidate universe of every compatib…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jiaojiao Jiang ·

    用于持续知识图谱嵌入中候选集干扰的匹配超排序正则化

    Continual knowledge graph embedding updates entity and relation representations as a graph grows. Existing methods primarily address catastrophic forgetting, but entity admission also changes the candidate universe of every compatible query. A historical answer can therefore lose…

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