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

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arxiv 2412.20005 v2 pith:U7FMJLBY submitted 2024-12-28 cs.CL cs.AIcs.DBcs.IRcs.LG

Yujie Luo , Xiangyuan Ru , Kangwei Liu , Lin Yuan , Mengshu Sun , Ningyu Zhang , Lei Liang , Zhiqiang Zhang
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Jun Zhou Lanning Wei Da Zheng Haofen Wang Huajun Chen
This is my paper · ORCID
classification cs.CLcs.AIcs.DBcs.IRcs.LG
keywords onekeknowledgeextractionagentsbasecaseconfiguredockerized
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs

    cs.CL 2025-09 conditional novelty 4.0 of 10

    A production system using ontology-guided prompts and two LLM stages extracted 19 million high-confidence facts from Wikipedia with 98.8% reported precision.

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