用频域自适应校准提升时序知识图谱未来预测精度
Denoising the Future: Context-Aware Spectral Diffusion for Temporal Knowledge Graph Extrapolation
- 将未来实体预测转为查询槽去噪，双流去噪器融合时间依赖与上下文感知
- 在4个公开数据集上达到当前最优性能，显著优于传统扩散模型
- 适合需要精准动态关系推演的研究者，尤其关注不确定性建模的场景
时序知识图谱（TKG）外推旨在从动态关系历史中推断未来事实。现有基于扩散的方法虽通过生成去噪改善了不确定性建模，但其对主语历史的聚合条件可能无法有效区分与查询相关的证据和非关键历史事实，从而稀释目标判别信号。为此，我们提出频域感知扩散框架FreqDiff。具体地，FreqDiff将未来对象预测建模为查询槽去噪，并设计双流去噪器，融合时间依赖建模与上下文感知频域校准。频域分支从可学习基底中合成历史条件滤波器，自适应重校准去噪表示；同时引入频域正则化项，使去噪结果在频域空间与真实目标对齐。在四个公开TKG基准上的实验表明，FreqDiff达到当前最优性能。
原文摘要 · Abstract (English)
Temporal Knowledge Graph (TKG) extrapolation seeks to infer future facts from time-varying relational histories. Recent diffusion-based approaches improve uncertainty modeling through generative denoising, but their aggregated conditioning on subject histories may insufficiently distinguish query-specific evidence from non-salient historical facts, thereby diluting target-discriminative signals. To bridge this gap, we propose FreqDiff, a Frequency-aware Diffusion framework for TKG extrapolation. Specifically, FreqDiff formulates future object prediction as query-slot denoising and develops a dual-stream denoiser that integrates temporal dependency modeling with context-aware spectral calibration. The spectral branch synthesizes history-conditioned filters from learnable bases to adaptively re-calibrate denoising representations, while a frequency-domain regularizer is proposed to align the denoised target with the gold object in spectral space. Experiments on four public TKG benchmarks demonstrate that FreqDiff achieves state-of-the-art performance.
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