ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs
Lei Sun Zhengwei Tao Youdi Li Hiroshi Arakawa
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%.