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        <title>KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models</title>
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        <namePart type="given">Jiho</namePart>
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        <namePart type="given">Yeonsu</namePart>
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    <name type="personal">
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.631</identifier>
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%0 Conference Proceedings
%T KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models
%A Kim, Jiho
%A Kwon, Yeonsu
%A Jo, Yohan
%A Choi, Edward
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kim-etal-2023-kg
%X 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.
%R 10.18653/v1/2023.findings-emnlp.631
%U https://aclanthology.org/2023.findings-emnlp.631
%U https://doi.org/10.18653/v1/2023.findings-emnlp.631
%P 9410-9421

Markdown (Informal)

[KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models](https://aclanthology.org/2023.findings-emnlp.631) (Kim et al., Findings 2023)

• KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models (Kim et al., Findings 2023)

ACL

• Jiho Kim, Yeonsu Kwon, Yohan Jo, and Edward Choi. 2023. KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9410–9421, Singapore. Association for Computational Linguistics.