mlsanityData to 7 Oct 2026

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

arXiv 2412.20005 ↗First posted 28 Dec 2024cs.CL cs.AI cs.DBcited by 8

On the map: LLM agents and tool use → Web, search, and research agents

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.

Key facts. Cited by 8 papers in the map (data through 7 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.

Builds on

7 of 10 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction3 Sep

Other references · 6

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  2. 2023ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPTcited by 250
  3. 2023Large Language Models for Generative Information Extraction: A Surveycited by 183
  4. 2021KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extractioncited by 180
  5. 2023InstructUIE: Multi-task Instruction Tuning for Unified Information Extractioncited by 167
  6. 2023Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulnesscited by 130

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 1.

8 papers.

  1. 2025SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflowscited by 12
  2. 2025Knowledge Augmented Complex Problem Solving with Large Language Models: A Surveycited by 6
  3. 2025ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMscited by 3
  4. 2025SemAgent: Semantic-Driven Agentic AI Empowered Trajectory Prediction in Vehicular Networkscited by 1
  5. 2025CFD-copilot: leveraging domain-adapted large language model and model context protocol to enhance simulation automationcited by 1
  6. 2026From Unstructured Recall to Schema-Grounded Memory: Reliable AI Memory via Iterative, Schema-Aware Extractioncited by 1
  7. 2025REMSA: Foundation Model Selection for Remote Sensing via a Constraint-Aware Agentcited by 0
  8. 2025From Retrieval to Reasoning: A Framework for Cyber Threat Intelligence NER with Explicit and Adaptive Instructionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Agents-K1: Towards Agent-native Knowledge Orchestration
  2. 2026Knows: Agent-Native Structured Research Representations
  3. 2026ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction
  4. 2026PACE: Publisher-Adaptive Content Extraction via Agentic Automation
  5. 2026Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki
  6. 2025TEXT2DB: Integration-Aware Information Extraction with Large Language Model Agents
  7. 2026Extracting Knowledge from Tools in LLM Agents
  8. 2026Schematize: An Agentic System for Generating and Refining Information-Extraction Schemas for Legal Research