{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/oneedit-a-neural-symbolic-collaboratively","title":"OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System","arxiv_id":"2409.07497","date":"2024-09-09","proceeding":null,"authors":["Ningyu Zhang","Zekun Xi","Yujie Luo","Peng Wang","Bozhong Tian","Yunzhi Yao","Jintian Zhang","Shumin Deng","Mengshu Sun","Lei Liang","Zhiqiang Zhang","Xiaowei Zhu","Jun Zhou","Huajun Chen"],"abstract":"Knowledge representation has been a central aim of AI since its inception. Symbolic Knowledge Graphs (KGs) and neural Large Language Models (LLMs) can both represent knowledge. KGs provide highly accurate and explicit knowledge representation, but face scalability issue; while LLMs offer expansive coverage of knowledge, but incur significant training costs and struggle with precise and reliable knowledge manipulation. To this end, we introduce OneEdit, a neural-symbolic prototype system for collaborative knowledge editing using natural language, which facilitates easy-to-use knowledge management with KG and LLM. OneEdit consists of three modules: 1) The Interpreter serves for user interaction with natural language; 2) The Controller manages editing requests from various users, leveraging the KG with rollbacks to handle knowledge conflicts and prevent toxic knowledge attacks; 3) The Editor utilizes the knowledge from the Controller to edit KG and LLM. 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