  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.
