MTE: Multi Transformation of Entities in Quaternion Vector Space for Temporal Knowledge Graph Completion

Ruiguo Yu, Jiazheng Guo, Mankun Zhao, Tianyi Xu, Jiujiang Guo, Wenbin Zhang, Mei Yu

Abstract: Compared with Static Knowledge Graphs, Temporal Knowledge Graphs need to pay more attention to the time when facts occur and these facts will change over time. However, existing models lack the capture of entity and relation and timestamp feature interactions, which is mainly reflected in the temporal multi-relation pattern and some relations exhibit persistence. To address the above issues, we propose a new TKGC model, which is Multi Transformation of Entities in Quaternion Vector Space (MTE). Specifically, we embed entities into 3D space and represent timestamps and relations as quaternions. MTE learns a pair of entity relation-aware vectors for each relation and an additional coordinate offset vector for each timestamp. In this way, MTE can capture the feature interactions between entities and relations and strengthen the interaction between entities and timestamps. Extensive experiment results show that MTE produces state-of-the-art performances on well-known benchmark datasets for Temporal Knowledge Graph Completion.