PTKE: Translation-based temporal knowledge graph embedding in polar coordinate system

Abstract

Knowledge graph embedding has received widespread attention in recent years. Most existing models represent time-independent facts as low dimensional embeddings. Nevertheless, knowledge graphs with temporal information provide more accurate and timely data. Hence, we propose Polar Temporal Knowledge Graph Embedding (PTKE), a novel temporal knowledge graph (TKG) embedding model which belongs to the translation-based model family and embeds time-aware facts into polar coordinate system. PTKE defines time as a constraint of the entity and synchronously embeds the starting and ending timestamps. The fact is divided into the modulus and the angular parts to avoid generating similar time-constrained entities. We use the modulus part to distinguish different time-constrained entities, and the angular part to distinguish time-constrained entities with the same modulus. Experiments on the temporal datasets show that PTKE outperforms prior state-of-the-art static knowledge graph (SKG) embedding models and temporal knowledge graph (TKG) embedding models in the link prediction task and the relation prediction task. Furthermore, the analysis of different time units and semantic expressive ability test on time embeddings prove that PTKE has a great ability on time expression.