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

arXiv: https://arxiv.org/abs/2412.20005 · HTML: https://mlsanity.com/paper/2412.20005/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 28 Dec 2024.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 7 of 7)

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023)
- [AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction](https://mlsanity.com/paper/2409.01854/) (2024)

## Built on by (most cited first, 8 of 8)

- [SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows](https://mlsanity.com/paper/2508.07935/) (2025)
- [Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey](https://mlsanity.com/paper/2505.03418/) (2025)
- [ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs](https://mlsanity.com/paper/2509.04696/) (2025)
- [REMSA: Foundation Model Selection for Remote Sensing via a Constraint-Aware Agent](https://mlsanity.com/paper/2511.17442/) (2025)
- [SemAgent: Semantic-Driven Agentic AI Empowered Trajectory Prediction in Vehicular Networks](https://mlsanity.com/paper/2512.00834/) (2025)
- [CFD-copilot: leveraging domain-adapted large language model and model context protocol to enhance simulation automation](https://mlsanity.com/paper/2512.07917/) (2025)
- [From Unstructured Recall to Schema-Grounded Memory: Reliable AI Memory via Iterative, Schema-Aware Extraction](https://mlsanity.com/paper/2604.27906/) (2026)
- [From Retrieval to Reasoning: A Framework for Cyber Threat Intelligence NER with Explicit and Adaptive Instructions](https://mlsanity.com/paper/2512.19414/) (2025)

## Abstract (arXiv)

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.