Tensor factorization for temporal knowledge graph forecasting

Tensor factorization has long been a cornerstone of knowledge graph (KG) reasoning, achieving state-of-the-art performance on static link prediction tasks with models such as ComplEx. Despite their effectiveness and scalability in KG reasoning, these approaches have been largely overlooked for temporal knowledge graph (TKG) forecasting, i.e., predicting links in future unseen timestamps, given historical facts in the form of quadruples (h,r,v,t), where t represents the timestamp of the relation. Most of the recent research has instead focused on deep architectures such as recurrent and graph neural networks, or transformers, which achieve strong accuracy but incur substantial computational costs at training and inference time. In this work, we revisit tensor factorization for TKG forecasting and investigate whether these lightweight models, if carefully designed and tuned, can rival deep learning architectures. Building on TNTComplEx, we propose an extended factorization model that incorporates a radial basis function (RBF) timestamp encoder to generate embeddings for unseen timestamps and a temporal regularizer to enforce smoothness in the embedding space. We conduct an extensive evaluation of tensor factorization methods for TKG forecasting, benchmarking on the five most common datasets in the literature. Our results show that tensor factorization models can achieve performance comparable to or exceeding that of state-of-the-art deep learning models, while being substantially more efficient in terms of training and inference time. Furthermore, our approach improves over previously reported factorization baselines by +5 to +30 MRR points. These findings position tensor factorization as a scalable and computationally attractive alternative for temporal knowledge graph forecasting, motivating further extensions to inductive reasoning on entities and relations.