ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs

• URL: http://arxiv.org/abs/2404.07677v2
• Date: Tue, 4 Jun 2024 07:16:14 GMT
• Title: ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs
• Authors: Lei Sun, Zhengwei Tao, Youdi Li, Hiroshi Arakawa,
• Abstract summary: We introduce Observation-Driven Agent (ODA), a novel AI framework tailored for tasks involving knowledge graphs (KGs) ODA incorporates KG reasoning abilities via global observation, which enhances reasoning capabilities through a cyclical paradigm of observation, action, and reflection. ODA demonstrates state-of-the-art performance on several datasets, notably achieving accuracy improvements of 12.87% and 8.9%.
• Score: 4.3508051546373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, which 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%.

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