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.
Feng et al. (Wed,) studied this question.