Temporal knowledge graph completion aims to predict missing entities and relationships over time. However, traditional methods often fail to effectively capture temporal dynamics and frequency-domain features. They struggle to properly weigh historical information across varying time intervals and overlook periodic patterns inherent in the data. To address these shortcomings, we introduce TDFT, a novel model that integrates time decay factors and frequency-domain transformations. TDFT incorporates a time decay mechanism to prioritize relevant historical information and uses frequency-domain transformations to uncover latent periodic features. These features are learned via a frequency-domain neural network, improving the model’s ability to model periodic fluctuations in entities and relationships. Experimental results show that TDFT outperforms existing methods on multiple benchmark datasets.
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