Summary of A Survey on Temporal Knowledge Graph: Representation Learning and Applications, by Li Cai et al.
A Survey on Temporal Knowledge Graph: Representation Learning and Applications
by Li Cai, Xin Mao, Yuhao Zhou, Zhaoguang Long, Changxu Wu, Man Lan
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive survey of temporal knowledge graph representation learning, which aims to model the dynamics of entities and relations over time. This subfield has gained significant attention due to the vast amount of structured knowledge that exists only within a specific period. The authors provide an introduction to the definitions, datasets, and evaluation metrics for temporal knowledge graph representation learning. They then propose a taxonomy based on core technologies and analyze various methods in each category. Finally, they discuss various downstream applications related to temporal knowledge graphs. This study will contribute to advancing our understanding of temporal knowledge graphs and their applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Temporal knowledge graphs are a type of knowledge graph that captures the evolution of entities and relations over time. The paper looks at how we can learn low-dimensional vector embeddings for these knowledge graphs, which is important because it can help us better understand how things change over time. The authors do a survey of different methods people have used to do this kind of learning and show how they work. They also talk about what these methods are good for and where they might be useful in the future. |
Keywords
» Artificial intelligence » Attention » Knowledge graph » Representation learning
