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English(EN) Biquaternionic Space with Complex-valued Attention for Temporal Knowledge Graph Completion

新型TKGE模型BSCA利用双四元数空间和注意力机制增强事实推理能力

研究人员开发了一种名为双四元数空间与复值注意力机制(BSCA)的新型时序知识图谱嵌入(TKGE)模型。该模型旨在通过利用一个统一的双四元数框架来改进演化知识图中缺失事实的推理,该框架结合了圆旋转和双曲旋转。BSCA包含一个复值注意力机制,能够自适应地融合时间条件和关系条件下的实体表示,使其能够随时间和关系上下文而变化。实验表明,BSCA在五个基准数据集上取得了具有竞争力的性能,尤其是在GDELT数据集上,其MRR达到了52.1%,比最强的基线模型高出14个百分点。 AI

影响 引入了一种新颖的知识图谱嵌入方法,有望提高AI理解和推理演化数据的能力。

排序理由 该集群包含一篇详细介绍新型时序知识图谱补全模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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新型TKGE模型BSCA利用双四元数空间和注意力机制增强事实推理能力

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该集群包含一篇详细介绍新型时序知识图谱补全模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rushan Geng, Cuicui Luo ·

    用于时序知识图谱补全的双四元数空间与复值注意力机制

    arXiv:2609.14279v1 Announce Type: cross Abstract: Temporal knowledge graph embedding (TKGE) models infer missing facts in knowledge graphs that evolve over time. Many existing models use a single geometric space, which can limit their ability to represent diverse relational patte…