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<p>This paper presents a state-of-the-art review analyzing the integration of Large Language Models (LLMs), Knowledge Graphs (KGs), and Reinforcement Learning (RL) in decision-making systems. We evaluate methodologies that enhance predictive accuracy and contextual coherence. Our findings support a hybrid KG-RL framework to improve performance through adaptable learning. This review synthesizes insights from 72 articles, providing a foundation for future research in this interdisciplinary field.</p>

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Large Language Models and Knowledge Graphs: A State-of-the-Art Exploration

  • 期刊论文

  • 发表日期：

    2025年8月18日

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  • Alí Aldo Gallardo-Delgado,
  • Juan Arturo Nolazco-Flores,
  • Ángeles Belem Priego-Sánchez,
  • Davide Buscaldi

摘要

This paper presents a state-of-the-art review analyzing the integration of Large Language Models (LLMs), Knowledge Graphs (KGs), and Reinforcement Learning (RL) in decision-making systems. We evaluate methodologies that enhance predictive accuracy and contextual coherence. Our findings support a hybrid KG-RL framework to improve performance through adaptable learning. This review synthesizes insights from 72 articles, providing a foundation for future research in this interdisciplinary field.