Hierarchy-Aware Temporal Knowledge Graph Embedding

Knowledge graph embedding has attracted widespread attention in recent years, and since knowledge graphs are dynamically updated in nature, the temporal information embedded is essential. Most of the knowledge graph embedding focuses on static KGs, while temporal knowledge graphs have been poorly studied. In the real-world, much structured knowledge is valid only within a specific temporality, i.e., the development of facts follows a temporal order. Therefore, more and more research works start to incorporate temporal information into knowledge graph representation learning, and the embedding of temporal knowledge graphs focuses on how to embed temporal information into the vector space. Most of the existing temporal knowledge graph embedding models do not model the semantic hierarchy, not fully exploiting the semantic information in the temporal knowledge graph. In this paper, we propose a hierarchy-aware temporal knowledge graph embedding (HA-TKGE), which maps temporal information into a polar coordinate system. The HA-TKGE is mainly inspired by the HAKE model. Specifically, the purpose of radial coordinates is to model temporal information at different levels, where entities with smaller radius are indicated at higher levels, and angular coordinates are intended to represent temporal information at the same level, which has approximately the same radial coordinates and different angles. The HA-TKGE model uses the nature of the polar coordinate system to represent the semantic hierarchy of temporal knowledge graphs and proves its effectiveness in the temporal node prediction task. Experiments show that the HA-TKGE model can effectively model the semantic hierarchy of temporal information and outperforms existing methods overall on the benchmark dataset for the temporal node prediction task.