Most algorithms for representation learning and link prediction on relational data are designed for static data. However, the data to which they are applied typically evolves over time, including online social networks or interactions between users and items in recommender systems. This is also the case for graph-structured knowledge bases -- knowledge graphs -- which contain facts that are valid only for specific points in time. In such contexts, it becomes crucial to correctly identify missing links at a precise time point, i.e. the temporal prediction link task. Recently, Lacroix et al. and Sadeghian et al. proposed a solution to the problem of link prediction for knowledge graphs under temporal constraints inspired by the canonical decomposition of 4-order tensors, where they regularise the representations of time steps by enforcing temporal smoothing, i.e. 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 smoothing regularisers using linear functions and recurrent architectures. In our experiments, we show that by carefully selecting the temporal smoothing regulariser and regularisation weight, a simple method like TNTComplEx can produce significantly more accurate results than state-of-the-art methods on three widely used temporal link prediction datasets. Furthermore, we evaluate the impact of a wide range of temporal smoothing regularisers on two state-of-the-art temporal link prediction models. Our work shows that simple tensor factorisation models can produce new state-of-the-art results using newly proposed temporal regularisers, highlighting a promising avenue for future research.

翻译：大多数针对关系数据的表示学习和链接预测算法都是为静态数据设计的。然而，这些算法所应用的数据通常会随时间演变，包括在线社交网络或推荐系统中用户与物品的交互。图结构知识库——知识图谱——也是如此，其中包含仅对特定时间点有效的事实。在此类情境下，正确识别特定时间点的缺失链接（即时序链接预测任务）变得至关重要。最近，Lacroix等人和Sadeghian等人受四阶张量规范分解的启发，提出了一种在时序约束下进行知识图谱链接预测的解决方案，该方法通过强制时序平滑（即学习相邻时间戳的相似变换）来正则化时间步的表示。然而，时序正则化项选择的影响仍未得到充分理解。在本工作中，我们系统分析了使用线性函数和循环架构的多种时序平滑正则化器选择。实验表明，通过仔细选择时序平滑正则化器及其权重，TNTComplEx这类简单方法能够在三个广泛使用的时序链接预测数据集上，产生比现有最优方法显著更准确的结果。此外，我们评估了多种时序平滑正则化器对两种前沿时序链接预测模型的影响。我们的研究表明，简单的张量分解模型结合新提出的时序正则化器能够取得新的最优结果，这为未来研究指明了一条有前景的路径。