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Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval

  • Yuan Zheng
  • Olli Seppänen
  • Sebastian Seiß
  • Jürgen Melzner

Recently there has been a strong interest in using semantic technologies to improve information management in the construction domain. Ontologies provide a formalized domain knowledge representation that provides a structured information model to facilitate information management issues such as formalization and integration of construction workflow information and data and enables further applications such as information retrieval and reasoning. SPARQL Protocol And RDF Query Language (SPARQL) queries are the main approaches to conduct the information retrieval from the Resource Description Framework (RDF) format data. However, there is a barrier for end users to develop the SPARQL queries, as it requires proficient skills to code them. This challenge hinders the practical application of ontology-based approaches on construction sites. As a generative language model, ChatGPT has already illustrated its capability to process and generate human-like text, including the capability to generate the SPARQL for domain-specific tasks. However, there are no specific tests evaluating and assessing the SPARQL-generating capability of ChatGPT within the construction domain. Therefore, this paper focuses on exploring the usage of ChatGPT with a case of importing the Digital Construction Ontologies (DiCon) and generating SPARQL queries for specific construction workflow information retrieval. We evaluate the generated queries with metrics including syntactical correctness, plausible query structure, and coverage of correct answers

  • Keywords:
  • Semantic web,
  • Ontology,
  • ChatGPT,
  • SPARQL,
  • RDF,
  • Information retrieval,
  • Construction,
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Yuan Zheng

Aalto University, Finland - ORCID: 0000-0002-9845-1138

Olli Seppänen

Aalto University, Finland - ORCID: 0000-0002-2008-5924

Sebastian Seiß

Bauhaus-University Weimar, Germany - ORCID: 0000-0001-5808-695X

Jürgen Melzner

Bauhaus-University Weimar, Germany - ORCID: 0000-0002-6435-0283

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  • Publication Year: 2023
  • Pages: 751-760

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  • Publication Year: 2023

Chapter Information

Chapter Title

Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval

Authors

Yuan Zheng, Olli Seppänen, Sebastian Seiß, Jürgen Melzner

DOI

10.36253/979-12-215-0289-3.75

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

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Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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