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%.

通过引入基于观察的智能体（ODA）框架，本研究旨在解决将大型语言模型（LLMs）和知识图谱（KGs）结合的问题，以提高 KG 的推理能力，并在多个数据集中展示了现实世界性能的显著提升（12.87% 和 8.9%）