KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using
Large Language Models
- URL: http://arxiv.org/abs/2310.11220v1
- Date: Tue, 17 Oct 2023 12:51:35 GMT
- Title: KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using
Large Language Models
- Authors: Jiho Kim, Yeonsu Kwon, Yohan Jo, Edward Choi
- Abstract summary: We propose KG-GPT, a framework leveraging large language models for tasks employing knowledge graphs.
KG-GPT comprises three steps: Sentence, Graph Retrieval, and Inference, each aimed at partitioning sentences, retrieving relevant graph components, and deriving logical conclusions.
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.
- Score: 18.20425100517317
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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