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English(EN) KG-Neighbor: Knowledge-Guided Neighborhood Construction for Embedding Visualization

新方法KG-Neighbor利用知识图谱改进嵌入可视化

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了KG-Neighbor，一种用于构建嵌入可视化中邻域图的新颖方法。该方法利用知识图谱（KGs）中的关系知识来调整嵌入距离，旨在更好地反映内容关系，而不仅仅是输入相似性。该方法不需要类别标签，还包括IDProfile来分析分配的类别与邻域多数之间的残差不匹配。跨各种数据集和编码器条件的实验表明，与标准的k近邻基线相比，KG-Neighbor在NMI和LT_2D等指标上有所改进，表明其在将嵌入邻域与语义内容对齐方面是有效的。 AI

影响 增强了嵌入可视化的可解释性，可能改进依赖于理解语义关系的任务。

排序理由 该集群包含一篇详细介绍嵌入可视化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新方法KG-Neighbor利用知识图谱改进嵌入可视化

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该集群包含一篇详细介绍嵌入可视化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chang Won Kim, Jaewook Lee, Haeun Min, JunNyeong Jeong, Minwoo Sim, Barom Kang, Kyohoon Jin ·

    KG-Neighbor：知识引导的邻域构建用于嵌入可视化

    arXiv:2611.07246v1 Announce Type: cross Abstract: In embedding visualizations, nearby instances are interpreted as semantically related, although neighborhoods derived from input embeddings may not reflect content relationships. Neighborhood-based dimensionality reduction (DR) me…

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