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Currently, large language models (LLMs) are the state of the art for pre-trained language models. LLMs have been applied to many tasks, including question and answering over Knowledge Graphs (KGs) and text-to-SPARQL, that is, the translation of Natural Language (NL) questions to SPARQL queries. This paper introduces Auto-KGQA, an autonomous domain-independent framework based on LLMs for text-to-SPARQL. The framework uses as context, fragments of the KG, which the LLM uses to translate the user’s NL question to a SPARQL query on the KG. Finally, it generates a natural language response for the user, based upon the result of the execution of SPARQL query over the KG.

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A Framework for Question Answering on Knowledge Graphs Using Large Language Models

  • 会议论文

  • 发表日期：

    2025年

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  • Caio Viktor S. Avila,
  • Marco A. Casanova,
  • Vânia M. P. Vidal

摘要

Currently, large language models (LLMs) are the state of the art for pre-trained language models. LLMs have been applied to many tasks, including question and answering over Knowledge Graphs (KGs) and text-to-SPARQL, that is, the translation of Natural Language (NL) questions to SPARQL queries. This paper introduces Auto-KGQA, an autonomous domain-independent framework based on LLMs for text-to-SPARQL. The framework uses as context, fragments of the KG, which the LLM uses to translate the user’s NL question to a SPARQL query on the KG. Finally, it generates a natural language response for the user, based upon the result of the execution of SPARQL query over the KG.