We introduce OneKE, a dockerized schema-guided knowledge extraction system, which can extract knowledge from the Web and raw PDF Books, and support various domains (science, news, etc.). Specifically, we design OneKE with multiple agents and a configure knowledge base. Different agents perform their respective roles, enabling support for various extraction scenarios. The configure knowledge base facilitates schema configuration, error case debugging and correction, further improving the performance. Empirical evaluations on benchmark datasets demonstrate OneKE's efficacy, while case studies further elucidate its adaptability to diverse tasks across multiple domains, highlighting its potential for broad applications. We have open-sourced the Code at https://github.com/zjunlp/OneKE and released a Video at http://oneke.openkg.cn/demo.mp4.
@article{arxiv.2412.20005,
title = {OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System},
author = {Yujie Luo and Xiangyuan Ru and Kangwei Liu and Lin Yuan and Mengshu Sun and Ningyu Zhang and Lei Liang and Zhiqiang Zhang and Jun Zhou and Lanning Wei and Da Zheng and Haofen Wang and Huajun Chen},
journal= {arXiv preprint arXiv:2412.20005},
year = {2025}
}