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

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 sys-tem. 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 tempo-ral 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 rela-tion 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. (c) 2023 Elsevier B.V. All rights reserved.