OneKE：一个基于Docker的基于Schema-Guided LLM代理的知识提取系统

OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System

December 28, 2024

作者: Yujie Luo, Xiangyuan Ru, Kangwei Liu, Lin Yuan, Mengshu Sun, Ningyu Zhang, Lei Liang, Zhiqiang Zhang, Jun Zhou, Lanning Wei, Da Zheng, Haofen Wang, Huajun Chen

cs.AI

摘要

我们介绍了OneKE，这是一个基于Docker的模式引导知识提取系统，可以从网络和原始PDF图书中提取知识，并支持各种领域（科学、新闻等）。具体来说，我们设计了OneKE，其中包括多个代理和一个配置知识库。不同的代理执行各自的角色，支持各种提取场景。配置知识库促进了模式配置、错误案例调试和修正，进一步提高了性能。在基准数据集上进行的实证评估显示了OneKE的有效性，而案例研究进一步阐明了其对跨多个领域的各种任务的适应性，突显了其广泛应用潜力。我们已在https://github.com/zjunlp/OneKE开源了代码，并发布了一个视频，网址为http://oneke.openkg.cn/demo.mp4。

English

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.