Enhancing neural link predictors for temporal knowledge graphs with temporal regularisers

The problem of link prediction in temporal knowledge graphs (TKGs) consists of finding missing links in the knowledge base under temporal constraints. Recently, [4] and [8] proposed a solution to the problem inspired by the canonical decomposition of 4-order tensors, where they regularise the representations of time steps by learning similar transformation for adjacent timestamps. However, the impact of the choice of temporal regularisation terms is still poorly understood. In this work, we systematically analyse several choices of temporal regularisers using linear functions and recurrent architectures. In our experiments, we show that by carefully selecting the temporal regulariser and regularisation weight, a simple method like TNTComplEx [4] can produce comparable results with state-of-the-art methods and enhance its original performance. Specifically, we observe that linear regularisers for temporal smoothing based on specific nuclear norms can significantly improve the predictive accuracy of the base temporal link prediction methods.