While large language models (LLMs) have made considerable advancements in understanding and generating unstructured text, their application in structured data remains underexplored. Particularly, using LLMs for complex reasoning tasks on knowledge graphs (KGs) remains largely untouched. To address this, we propose KG-GPT, a multi-purpose framework leveraging LLMs for tasks employing KGs. KG-GPT comprises three steps: Sentence Segmentation, Graph Retrieval, and Inference, each aimed at partitioning sentences, retrieving relevant graph components, and deriving logical conclusions, respectively. We evaluate KG-GPT using KG-based fact verification and KGQA benchmarks, with the model showing competitive and robust performance, even outperforming several fully-supervised models. Our work, therefore, marks a significant step in unifying structured and unstructured data processing within the realm of LLMs.

翻译：摘要：尽管大语言模型（LLMs）在理解和生成非结构化文本方面取得了显著进展，但它们在结构化数据中的应用仍待探索。特别是，利用LLMs执行知识图谱（KGs）上的复杂推理任务仍基本未被触及。为解决这一问题，我们提出了KG-GPT，一个多用途框架，可利用LLMs完成涉及KGs的任务。KG-GPT包含三个步骤：句子分割、图谱检索和推理，分别用于拆分句子、检索相关图组件并推导出逻辑结论。我们通过基于KGs的事实验证和KGQA基准评估了KG-GPT，结果表明该模型具有竞争性和稳健性，其性能甚至超越了多个全监督模型。因此，我们的工作标志着在LLMs领域内统一结构化与非结构化数据处理迈出了重要一步。