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

**Authors:** Xin Xie, Zhoubo Li, Xiaohan Wang, Shumin Deng, Feiyu Xiong, Huajun Chen, Ningyu Zhang  
**arXiv:** [2210.00305](https://arxiv.org/abs/2210.00305)  
**Date:** 2022-10-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2210.00305v1/

## Key points

- PromptKG is an open-sourced comprehensive framework for Knowledge Graphs (KGs)
- It supports KGs with heterogeneous graph structures and text-rich entity/relation information
- It integrates cutting-edge text-based methods and a new prompt learning model
- Supports various tasks such as knowledge graph completion, question answering, recommendation, and knowledge probing
- Authors of the paper are Xin Xie, Zhoubo Li, Xiaohan Wang, Shumin Deng, Feiyu Xiong, Huajun Chen, and Ningyu Zhang
- The framework is publicly available at https://github.com/zjunlp/PromptKG
- Offers long-term technical support.

## Summary

PromptKG is an open-sourced comprehensive framework designed specifically for Knowledge Graphs (KGs) with heterogeneous graph structures and text-rich entity/relation information. It integrates cutting-edge text-based methods and a new prompt learning model to support various tasks such as knowledge graph completion, question answering, recommendation, and knowledge probing. The authors of the paper are Xin Xie, Zhoubo Li, Xiaohan Wang, Shumin Deng, Feiyu Xiong, Huajun Chen and Ningyu Zhang. The framework is publicly available at https://github.com/zjunlp/PromptKG and offers long-term technical support.