TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation
This addresses the need for reasoning over time in knowledge graphs, which is an incremental improvement for applications like recommendation systems and question answering.
The paper tackles the problem of learning representations for temporal knowledge graphs (TKGs) by proposing TeRo, a time-aware embedding model that defines temporal evolution as rotation in complex vector space, and it significantly outperforms existing state-of-the-art models for link prediction on four TKGs.
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.