Survey on Temporal Knowledge Graph

Knowledge Graph built by people is usually represented as a network with nodes representing entities and edges representing relations between entities. People need to use this form of network architecture to fill in the missing facts in the knowledge graph. Knowledge graph plays an important role in natural language processing. Link prediction task has been studied for a long time as one of the most important tasks in Knowledge Graph Reasoning. Most existing methods focus on static knowledge graph, which cannot get the temporal information in knowledge graph. Now some methods study on temporal knowledge graph, where each fact is associated with a timestamp. The task also becomes more challenging on temporal knowledge graph. In this paper, we introduce some models on static knowledge graph and temporal knowledge graph, and analyze the advantages and disadvantages of each model.