双四元数空间与复值注意力用于时序知识图谱补全
Biquaternionic Space with Complex-valued Attention for Temporal Knowledge Graph Completion
- University of Chinese Academy of Sciences（中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出BSCA模型，在双四元数空间中结合圆与双曲旋转，用复值注意力动态融合实体表示，在GDELT等基准上显著提升时序知识图谱补全性能。
AI中文摘要：
时序知识图谱嵌入（TKGE）模型用于推断随时间演化的知识图谱中缺失的事实。许多现有模型使用单一几何空间，这限制了它们表示多样关系模式的能力，或将实体表示视为静态的。我们提出双四元数空间与复值注意力（BSCA），一种在统一双四元数框架内结合圆形旋转和双曲旋转的TKGE模型。复值注意力机制自适应地融合时间条件和关系条件下的实体表示，使它们能够随时间和关系上下文变化。在五个基准数据集上的实验显示，BSCA在各数据集上表现具有竞争力，其中在GDELT上提升最大：BSCA的MRR达到52.1%，而对比中最强基线的MRR为38.1%。
英文摘要：
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 patterns, or treat entity representations as static. We propose Biquaternionic Space with Complex-valued Attention (BSCA), a TKGE model that combines circular and hyperbolic rotations within a unified biquaternionic framework. A complex-valued attention mechanism adaptively fuses time-conditioned and relation-conditioned entity representations, allowing them to vary with temporal and relational context. Experiments on five benchmark datasets show competitive performance across datasets, with the largest improvement on GDELT: BSCA achieves an MRR of 52.1\%, compared with 38.1\% for the strongest baseline in our comparison.
