A Survey on Temporal Knowledge Graph: Representation Learning and
  Applications
Li Cai Xin Mao Yuhao Zhou Zhaoguang Long Changxu Wu Man Lan
Knowledge graphs have garnered significant research attention and are widely
used to enhance downstream applications. However, most current studies mainly
focus on static knowledge graphs, whose facts do not change with time, and
disregard their dynamic evolution over time. As a result, temporal knowledge
graphs have attracted more attention because a large amount of structured
knowledge exists only within a specific period. Knowledge graph representation
learning aims to learn low-dimensional vector embeddings for entities and
relations in a knowledge graph. The representation learning of temporal
knowledge graphs incorporates time information into the standard knowledge
graph framework and can model the dynamics of entities and relations over time.
In this paper, we conduct a comprehensive survey of temporal knowledge graph
representation learning and its applications. We begin with an introduction to
the definitions, datasets, and evaluation metrics for temporal knowledge graph
representation learning. Next, we propose a taxonomy based on the core
technologies of temporal knowledge graph representation learning methods, and
provide an in-depth analysis of different methods in each category. Finally, we
present various downstream applications related to the temporal knowledge
graphs. In the end, we conclude the paper and have an outlook on the future
research directions in this area.