Denoising the Future: Context-Aware Spectral Diffusion for Temporal Knowledge Graph Extrapolation

Abstract: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.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.20804 [cs.CL]

(or arXiv:2608.20804v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.20804 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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