The following contribution introduces a concept that employs Large Language Models (LLMs) and a chatbot interface to enhance SPARQL query generation for ontologies, thereby facilitating intuitive access to formalized knowledge. Utilizing natural language inputs, the system converts user inquiries into accurate SPARQL queries that strictly query the factual content of the ontology, effectively preventing misinformation or fabrication by the LLM. To enhance the quality and precision of outcomes, additional textual information from established domain-specific standards is integrated into the ontology for precise descriptions of its concepts and relationships. An experimental study assesses the accuracy of generated SPARQL queries, revealing significant benefits of using LLMs for querying ontologies and highlighting areas for future research.

翻译：本文提出一种利用大型语言模型（LLMs）和聊天机器人界面来增强面向本体的SPARQL查询生成的概念，从而促进对形式化知识的直观访问。该系统通过自然语言输入，将用户查询转换为精确的SPARQL查询语句，这些语句严格查询本体的事实内容，有效防止了LLM产生错误信息或虚构内容。为提高结果的质量与精确度，系统将来自既定领域特定标准的额外文本信息整合到本体中，以对其概念和关系进行精确描述。一项实验研究评估了所生成SPARQL查询的准确性，揭示了使用LLMs查询本体的显著优势，并指明了未来研究的重点方向。