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As part of knowledge engineering workflows, semantic artifacts, such as ontologies, knowledge graphs or semantic descriptions based on industrial standards, are often validated in terms of their compliance with requirements expressed in natural language (e.g., ontology competency questions, standard specifications). Key to this process is the translation of the requirements in machine-actionable queries (e.g., SPARQL) that can automate the validation process. This manual translation process is time-consuming, error-prone and challenging, especially in areas where domain experts might lack knowledge of semantic technologies. In this paper, we propose a Large Language Models (LLMs) based approach to translate requirements texts into SPARQL queries and test it in validation use cases related to SAREF and OPC UA Robotics. F1 scores of 88–100% indicate the feasibility of the approach and its potential impact on ensuring high quality semantic artifacts and further uptake of the semantic technologies (industrial) domains.

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Validating Semantic Artifacts with Large Language Models

  • 会议论文

  • 发表日期：

    2025年

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  • Nilay Tufek,
  • Aparna Saissre Thuluva,
  • Valentin Philipp Just,
  • Fajar J. Ekaputra,
  • Tathagata Bandyopadhyay,
  • Marta Sabou,
  • Allan Hanbury

摘要

As part of knowledge engineering workflows, semantic artifacts, such as ontologies, knowledge graphs or semantic descriptions based on industrial standards, are often validated in terms of their compliance with requirements expressed in natural language (e.g., ontology competency questions, standard specifications). Key to this process is the translation of the requirements in machine-actionable queries (e.g., SPARQL) that can automate the validation process. This manual translation process is time-consuming, error-prone and challenging, especially in areas where domain experts might lack knowledge of semantic technologies. In this paper, we propose a Large Language Models (LLMs) based approach to translate requirements texts into SPARQL queries and test it in validation use cases related to SAREF and OPC UA Robotics. F1 scores of 88–100% indicate the feasibility of the approach and its potential impact on ensuring high quality semantic artifacts and further uptake of the semantic technologies (industrial) domains.