让非技术人员用自然语言操作知识图谱，实现多轮对话与动态更新。
AGENTiGraph: A Multi-Agent Knowledge Graph Framework for Interactive, Domain-Specific LLM Chatbots
- 基于多智能体框架，通过自然语言直接构建和修改知识图谱。
- 在教育场景下达成95.12%分类准确率与90.45%执行成功率。
- 适合法律、医疗等需实时更新复杂规则的领域使用。
AGENTiGraph 是一个面向非技术用户的智能系统，支持通过自然语言交互与管理特定领域的数据，核心是知识图谱的直观操作。该系统无需专用查询语言，即可实现多轮对话与动态更新，具备意图识别、任务规划与自动知识融合能力，保障跨任务间的流畅推理。在包含3,500个查询的教育场景基准测试中，系统表现优于强零样本基线（分类准确率达95.12%，执行成功率为90.45%），展现出在合规性要求高或需多步推理的法律、医疗等领域扩展潜力，例如实时纳入新法规或研究进展。开源演示展示了大模型与结构化图谱结合的新范式，适用于多轮企业级知识管理。
原文摘要 · Abstract (English)
AGENTiGraph is a user-friendly, agent-driven system that enables intuitive interaction and management of domain-specific data through the manipulation of knowledge graphs in natural language. It gives non-technical users a complete, visual solution to incrementally build and refine their knowledge bases, allowing multi-round dialogues and dynamic updates without specialized query languages. The flexible design of AGENTiGraph, including intent classification, task planning, and automatic knowledge integration, ensures seamless reasoning between diverse tasks. Evaluated on a 3,500-query benchmark within an educational scenario, the system outperforms strong zero-shot baselines (achieving 95.12% classification accuracy, 90.45% execution success), indicating potential scalability to compliance-critical or multi-step queries in legal and medical domains, e.g., incorporating new statutes or research on the fly. Our open-source demo offers a powerful new paradigm for multi-turn enterprise knowledge management that bridges LLMs and structured graphs.
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