Temporal knowledge graph completion based on time embedding and time-frequency decoder

Temporal knowledge graph (TKG) completion is essential for predicting missing links by modeling the evolution of entities and relations over time, with critical applications in areas like event forecasting and trend analysis. However, existing methods often prioritize entity and relationship dynamics, treating temporal information as a secondary feature, which hampers their ability to effectively capture changes across diverse time scales. This limitation restricts their capacity to model complex temporal patterns, such as periodicity and long-term trends, vital for representing time-sensitive data accurately. To overcome these challenges, we propose TETFD (time embedding and time-frequency decoder), a novel method that incorporates time embeddings to explicitly capture temporal dynamics and integrates them into a specialized decoder. This decoder not only processes entity and relationship data but also incorporates frequency domain features extracted from temporal sequences, enhancing the model’s ability to reflect both short-term variations and prolonged trends. Experimental results across five benchmark datasets demonstrate that TETFD significantly outperforms existing approaches, establishing new standards in TKG completion tasks.