用于神经链接预测器的时序平滑正则化器
Temporal Smoothness Regularisers for Neural Link Predictors
AI总结：
本文提出了一种基于时间平滑正则化器的神经链接预测方法，在三个数据集上实现了更准确的结果。
摘要：
大多数用于关系数据表示学习和链接预测的算法都是为静态数据设计的。然而，它们通常应用于不断演变的数据，包括在线社交网络或推荐系统中用户与物品之间的互动。这种情况也适用于图结构知识库——知识图谱——这些图谱包含仅在特定时间点有效的事实。在这种情况下，正确地在特定时间点识别缺失链接变得至关重要，即时间预测链接任务。最近，Lacroix等人和Sadeghian等人提出了一个受四阶张量分解启发的解决方案，用于在时间约束下进行知识图谱的链接预测问题，他们通过强制时间步骤的表示平滑来正则化表示，即通过学习相邻时间戳的相似转换。然而，时间正则化项的选择影响仍然不明确。在本工作中，我们系统地分析了几种时间平滑正则化器的选择，使用线性函数和递归架构。在我们的实验中，我们展示了通过精心选择时间平滑正则化器和正则化权重，简单的TNTComplEx方法可以在三个广泛使用的时序链接预测数据集上产生比最先进方法显著更准确的结果。此外，我们评估了多种时间平滑正则化器对两个最先进的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可以通过新提出的时序正则化器产生新的最先进结果，这为未来研究指明了有前途的方向。
英文摘要：
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.
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