In the last few years, there has been a surge of interest in learning
representations of entitiesand relations in knowledge graph (KG). However, the
recent availability of temporal knowledgegraphs (TKGs) that contain time
information for each fact created the need for reasoning overtime in such TKGs.
In this regard, we present a new approach of TKG embedding, TeRo, which defines
the temporal evolution of entity embedding as a rotation from the initial time
to the currenttime in the complex vector space. Specially, for facts involving
time intervals, each relation isrepresented as a pair of dual complex
embeddings to handle the beginning and the end of therelation, respectively. We
show our proposed model overcomes the limitations of the existing KG embedding
models and TKG embedding models and has the ability of learning and
inferringvarious relation patterns over time. Experimental results on four
different TKGs show that TeRo significantly outperforms existing
state-of-the-art models for link prediction. In addition, we analyze the effect
of time granularity on link prediction over TKGs, which as far as we know
hasnot been investigated in previous literature.

本文提出了基于复向量空间的实体嵌入旋转方法 TeRo 用于处理包含时间信息的知识图谱（TKG），结合对起止时间进行双重嵌入，解决了现有 KG 嵌入模型的局限性，通过在不同 TKG 数据集上进行实验，TeRo 在链接预测方面表现出优异性能，同时分析了时间粒度对于链接预测的作用。