The integration of Large Language Models (LLMs) and knowledge graphs (KGs) has achieved remarkable success in various natural language processing tasks. However, existing methodologies that integrate LLMs and KGs often navigate the task-solving process solely based on the LLM's analysis of the question, overlooking the rich cognitive potential inherent in the vast knowledge encapsulated in KGs. To address this, we introduce Observation-Driven Agent (ODA), a novel AI agent framework tailored for tasks involving KGs. ODA incorporates KG reasoning abilities via global observation that enhances reasoning capabilities through a cyclical paradigm of observation, action, and reflection. Confronting the exponential explosion of knowledge during observation, we innovatively design a recursive observation mechanism. Subsequently, we integrate the observed knowledge into the action and reflection modules. Through extensive experiments, ODA demonstrates state-of-the-art performance on several datasets, notably achieving accuracy improvements of 12.87% and 8.9%.

翻译：大语言模型（LLMs）与知识图谱（KGs）的集成已在多种自然语言处理任务中取得显著成功。然而，现有集成LLMs与KGs的方法通常仅基于LLM对问题的分析来规划任务求解过程，忽视了KG所蕴含的丰富认知潜力。为此，我们提出观测驱动型智能体（ODA），一种面向KG任务的新型人工智能智能体框架。ODA通过全局观测融入KG推理能力，借助观测、行动与反思的循环范式增强推理效能。针对观测过程中知识呈指数级爆炸的挑战，我们创新性地设计了递归观测机制，进而将观测所得知识整合至行动与反思模块。大量实验表明，ODA在多个数据集上达到了最先进性能，尤其实现了12.87%和8.9%的准确率提升。