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.

KG-GPT 是一个多功能框架，利用大型语言模型（LLMs）进行知识图谱（KGs）相关任务的复杂推理，将句子分割、检索相关图谱组件和推导逻辑结论分为三个步骤。我们通过使用基于 KG 的事实验证和 KGQA 基准来评估 KG-GPT，发现该模型表现出竞争力和稳健性，甚至胜过几个完全监督的模型。因此，我们的工作在统一 LLMs 的结构化和非结构化数据处理方面迈出了重要的一步。