PromptKG: A Prompt Learning Framework for Knowledge Graph Representation Learning and Application

@article{Xie2022PromptKGAP,
  title={PromptKG: A Prompt Learning Framework for Knowledge Graph Representation Learning and Application},
  author={Xin Xie and Zhoubo Li and Xiaohan Wang and Shumin Deng and Feiyu Xiong and Huajun Chen and Ningyu Zhang},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.00305}
}
Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. KG representation models should consider graph structures and text se-mantics, but no comprehensive open-sourced framework is mainly designed for KG regarding informative text description. In this pa-per, we present PromptKG , a prompt learning framework for KG representation learning and application that equips the cutting-edge text-based methods, integrates a new… 

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