OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction
  System
Authors: name: Yujie Luo; name: Xiangyuan Ru; name: Kangwei Liu; name: Lin Yuan; name: Mengshu Sun; name: Ningyu Zhang; name: Lei Liang; name: Zhiqiang Zhang; name: Jun Zhou; name: Lanning Wei; name: Da Zheng; name: Haofen Wang; name: Huajun Chen
Published: 2024-12-28 04:01:30

Abstract: 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.