用自然语言协同编辑知识图谱与大模型，提升准确性与安全性。
OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
- 融合符号系统与大模型，通过自然语言实现知识协同编辑
- 支持多用户操作，具备回滚机制防止冲突与恶意攻击
- 在两个新数据集上表现优于现有方法，适合知识管理场景
知识表示一直是人工智能的核心目标。符号知识图谱（KG）和神经大型语言模型（LLM）均可表示知识：KG提供高精度、显式的知识表达，但存在可扩展性问题；而LLM覆盖范围广，但训练成本高，且难以精确可靠地进行知识操作。为此，我们提出OneEdit，一个基于自然语言的神经符号协同知识编辑原型系统，支持使用知识图谱与大模型进行易用的知识管理。OneEdit包含三个模块：1）解释器用于用户以自然语言交互；2）控制器利用知识图谱及回滚机制管理来自不同用户的编辑请求，处理知识冲突并防止有毒知识攻击；3）编辑器根据控制器提供的知识更新知识图谱与大模型。我们在两个新构建的知识图谱数据集上进行实验，结果表明OneEdit能取得优越性能。
原文摘要 · Abstract (English)
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. We conduct experiments on two new datasets with KGs which demonstrate that OneEdit can achieve superior performance.
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