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English(EN) Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs

新的神经回归模型增强了知识图谱属性预测能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为LitEm的新型神经回归模型，用于预测知识图谱中的数值属性。该模型与现有的归纳式知识图谱嵌入技术相结合，增强了它们表示多样化现实世界数据的能力。实验表明，LitEm在多个数据集上表现具有竞争力，并且将LitEm与最先进的嵌入模型相结合的协同训练框架提高了链接预测性能，并实现了数值属性预测。 AI

影响 这项研究可以提高知识图谱表示的准确性和完整性，从而惠及依赖结构化数据的下游AI应用。

排序理由 该集群包含一篇详细介绍用于知识图谱属性预测的新模型的学术论文。[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 →

新的神经回归模型增强了知识图谱属性预测能力

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该集群包含一篇详细介绍用于知识图谱属性预测的新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Rupesh Sapkota, Louis Mozart Kamdem Teyou, Moshood Yekini, Caglar Demir, Axel-Cyrille Ngonga Ngomo ·

    知识图谱中用于数值属性预测的带嵌入的神经回归

    arXiv:2608.26729v1 Announce Type: new Abstract: In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering. Although knowledge graphs often contain rich numerical attributes, most embedding models neglect …

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