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.

翻译：本文介绍OneKE，一个基于Docker的模式引导知识抽取系统，该系统能够从网页和原始PDF书籍中抽取知识，并支持多种领域（科学、新闻等）。具体而言，我们设计了包含多个智能体和一个可配置知识库的OneKE架构。不同的智能体执行各自的角色，从而支持多样化的抽取场景。可配置知识库便于模式配置、错误案例调试与修正，进一步提升了系统性能。在基准数据集上的实证评估验证了OneKE的有效性，同时案例研究进一步阐明了其在跨多个领域的多样化任务中的适应能力，凸显了其广泛应用的潜力。我们已在https://github.com/zjunlp/OneKE开源代码，并在http://oneke.openkg.cn/demo.mp4发布了演示视频。