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Tensor factorization for temporal knowledge graph forecasting
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DOI：10.1016/j.neucom.2026.132846.png)
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Abstract
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• We revisit tensor factorization for temporal knowledge graph forecasting. • We extend TNTComplEx with an RBF timestamp encoder and a temporal regularizer. • Our model achieves comparable or superior performance to state-of-the-art deep models. • We report 5 to 30 MRR improvements over previous tensor factorization baselines. • The method is substantially more efficient in training and inference time compared to state-of-the-art graph neural network models.
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