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In the last few years, there has been a surge of interest in learningrepresentations of entitiesand relations in knowledge graph (KG). However, therecent availability of temporal knowledgegraphs (TKGs) that contain timeinformation 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 definesthe temporal evolution of entity embedding as a rotation from the initial timeto the currenttime in the complex vector space. Specially, for facts involvingtime intervals, each relation isrepresented as a pair of dual complexembeddings to handle the beginning and the end of therelation, respectively. Weshow our proposed model overcomes the limitations of the existing KG embeddingmodels and TKG embedding models and has the ability of learning andinferringvarious relation patterns over time. Experimental results on fourdifferent TKGs show that TeRo significantly outperforms existingstate-of-the-art models for link prediction. In addition, we analyze the effectof time granularity on link prediction over TKGs, which as far as we knowhasnot been investigated in previous literature.

TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation