  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.
