OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System
This system addresses the need for adaptable knowledge extraction across various domains, but it appears incremental as it builds on existing agent-based and schema-guided approaches.
The authors tackled the problem of extracting knowledge from diverse sources like the Web and PDF books across multiple domains by introducing OneKE, a dockerized schema-guided LLM agent-based system, which demonstrated efficacy in empirical evaluations on benchmark datasets.
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.