发表机构
Insilicom（硅基公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对时间知识图谱预测未来事件的难题，提出群组注意力神经霍克斯过程（GAttNHP）框架，通过自注意力编码器、语义软分组模块和非交叉分位数回归头，在实体预测和时间预测上优于基线，尤其在长尾事件链表现突出。
AI 中文摘要
时间知识图谱（TKGs）记录事实如何随时间演变，但预测未来事件仍很困难，原因有三：难以编码长程时间依赖；不同链上事件相互激发或抑制，快照级模型无法表达；到达间隔时间重尾且统计稀疏，确定性时间预测器不可靠。我们用单一框架群组注意力神经霍克斯过程（GAttNHP）解决这三个问题，它由三个匹配组件构成。自注意力编码器将每个主题 - 关系链转换为连续时间点过程并捕捉遥远历史的持久激发；语义软分组模块将全局可学习的霍克斯先验转换为分析交叉注意力掩码；非交叉分位数（NCQ）回归头取代基于均值的时间预测。在六个基准TKG数据集上，GAttNHP在实体预测和时间预测方面均优于现有基线，消融实验证实其在现有模型最易失败的长尾事件链上收益最大。
英文摘要
Temporal Knowledge Graphs (TKGs) record how facts evolve over time, but forecasting future events on a TKG remains difficult for three reasons: (i) long-range temporal dependencies are hard to encode; (ii) events on different chains mutually excite or inhibit one another in ways that snapshot-level models cannot express; and (iii) inter-arrival times are heavy-tailed and statistically sparse, so deterministic time predictors are unreliable. We address these three issues with a single framework, the \textbf{Group Attention Neural Hawkes Process (GAttNHP)}, built around three matched components. First, a self-attention encoder casts each subject--relation chain as a continuous-time point process and captures the lingering excitation of distant history. Second, a semantic soft-grouping module turns globally learnable Hawkes priors into an analytical cross-attention mask, so chains share excitation patterns through their latent group memberships rather than through exhaustive pairwise computation. Third, a Non-Crossing Quantile (NCQ) regression head replaces mean-based time prediction, providing calibrated, monotonically ordered quantile estimates that remain stable under heavy-tailed inter-arrival distributions. On six benchmark TKG datasets, GAttNHP improves over state-of-the-art baselines on both entity prediction and time prediction, and ablations confirm that its largest gains arise on the long-tail event chains where existing models fail most severely.
