Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static graphs. In this paper, we study the challenging problem of inference over temporal knowledge graphs. In particular, the task of temporal link prediction. In general, this is a difficult task due to data non-stationarity, data heterogeneity, and its complex temporal dependencies. We propose Chronological Rotation embedding (ChronoR), a novel model for learning representations for entities, relations, and time. Learning dense representations is frequently used as an efficient and versatile method to perform reasoning on knowledge graphs. The proposed model learns a k-dimensional rotation transformation parametrized by relation and time, such that after each fact's head entity is transformed using the rotation, it falls near its corresponding tail entity. By using high dimensional rotation as its transformation operator, ChronoR captures rich interaction between the temporal and multi-relational characteristics of a Temporal Knowledge Graph. Experimentally, we show that ChronoR is able to outperform many of the state-of-the-art methods on the benchmark datasets for temporal knowledge graph link prediction.

中文翻译：

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ChronoR：基于旋转的时间知识图嵌入

尽管时间知识图的重要性和丰富性，但当前的大多数研究都集中在静态图的推理上。在本文中，我们研究了基于时间知识图的推理的挑战性问题。特别是时间链接预测的任务。通常，由于数据不稳定，数据异质性及其复杂的时间依赖性，这是一项艰巨的任务。我们提出了时间轮转嵌入（ChronoR），这是一种用于学习实体，关系和时间表示的新颖模型。学习密集表示经常被用作在知识图上进行推理的一种有效且通用的方法。提出的模型学习了一种由关系和时间参数化的k维旋转变换，以便在使用旋转对每个事实的头部实体进行变换之后，它落在其相应的尾部实体附近。通过使用高维旋转作为其变换算符，ChronoR捕获了时间知识图的时间和多关系特征之间的丰富交互。通过实验，我们证明了ChronoR能够胜过基准数据集上用于时态知识图链接预测的许多最新方法。