OneKE：一個使用Docker容器化的基於Schema-Guided LLM代理的知識提取系統

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

我們介紹了 OneKE，一個以 Docker 包裝的基於架構引導的知識提取系統，可以從網絡和原始 PDF 書籍中提取知識，並支持各種領域（科學、新聞等）。具體來說，我們設計了 OneKE，其中包括多個代理和配置知識庫。不同的代理執行其各自的角色，實現對各種提取場景的支持。配置知識庫促進了架構配置、錯誤情況的調試和修正，進一步提高了性能。對基準數據集的實證評估顯示了 OneKE 的有效性，而案例研究進一步闡明了其對多個領域的各種任務的適應性，突顯了其廣泛應用的潛力。我們已在 https://github.com/zjunlp/OneKE 上開源了代碼，並發布了一個視頻，網址為 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.