Summary of Oneke: a Dockerized Schema-guided Llm Agent-based Knowledge Extraction System, by Yujie Luo et al.
OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System
by 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
First submitted to arxiv on: 28 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The OneKE system is a schema-guided knowledge extraction tool that can extract information from the web and PDF books across various domains, including science and news. It uses multiple agents to perform different roles and supports diverse extraction scenarios through its configure knowledge base. The system’s performance is improved through error case debugging and correction. Empirical evaluations demonstrate OneKE’s efficacy, while case studies showcase its adaptability across multiple domains. The tool has the potential for broad applications and is open-sourced at GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OneKE is a machine that helps find important information on the internet and in books. It can do this by looking at websites and PDF files from many different fields like science, news, and more. The system uses special helpers called agents to make sure it gets the right information. This helps OneKE work well even when there are problems or mistakes. People have tested OneKE and shown that it works well in many situations. It can be used for lots of things and is free for anyone to use. |
Keywords
» Artificial intelligence » Knowledge base
