Chapter Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval
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 form...
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| Format: | Online |
| Langue: | anglais |
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Firenze University Press
2024
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| Accès en ligne: | ONIX_20240402_9791221502893_26 |
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| _version_ | 1869525173908013056 |
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| author | Zheng, Yuan Seppänen, Olli Seiß, Sebastian Melzner, Jürgen |
| author_browse | Melzner, Jürgen Seiß, Sebastian Seppänen, Olli Zheng, Yuan |
| author_facet | Zheng, Yuan Seppänen, Olli Seiß, Sebastian Melzner, Jürgen |
| author_sort | Zheng, Yuan |
| collection | Directory of Open Access Books |
| description | 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 |
| format | Online |
| id | doab-20.500.12854ir-137103 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1371032024-05-12T04:29:03Z Chapter Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval Zheng, Yuan Seppänen, Olli Seiß, Sebastian Melzner, Jürgen Semantic web Ontology ChatGPT SPARQL RDF Information retrieval Construction thema EDItEUR::U Computing and Information Technology 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 2024-05-12T04:29:01Z 2024-05-12T04:29:01Z 2024-04-02T15:45:07Z 2023 chapter ONIX_20240402_9791221502893_26 2704-5846 https://library.oapen.org/handle/20.500.12657/89057 9791221502893 https://directory.doabooks.org/handle/20.500.12854/137103 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89057/1/9791221502893_75.pdf Firenze University Press 10.36253/979-12-215-0289-3.75 10.36253/979-12-215-0289-3.75 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 10 Florence open access |
| spellingShingle | Semantic web Ontology ChatGPT SPARQL RDF Information retrieval Construction thema EDItEUR::U Computing and Information Technology Zheng, Yuan Seppänen, Olli Seiß, Sebastian Melzner, Jürgen Chapter Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval |
| title | Chapter Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval |
| title_full | Chapter Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval |
| title_fullStr | Chapter Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval |
| title_full_unstemmed | Chapter Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval |
| title_short | Chapter Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval |
| title_sort | chapter testing chatgpt aided sparql generation for semantic construction information retrieval |
| topic | Semantic web Ontology ChatGPT SPARQL RDF Information retrieval Construction thema EDItEUR::U Computing and Information Technology |
| topic_facet | Semantic web Ontology ChatGPT SPARQL RDF Information retrieval Construction thema EDItEUR::U Computing and Information Technology |
| url | ONIX_20240402_9791221502893_26 |
| work_keys_str_mv | AT zhengyuan chaptertestingchatgptaidedsparqlgenerationforsemanticconstructioninformationretrieval AT seppanenolli chaptertestingchatgptaidedsparqlgenerationforsemanticconstructioninformationretrieval AT seißsebastian chaptertestingchatgptaidedsparqlgenerationforsemanticconstructioninformationretrieval AT melznerjurgen chaptertestingchatgptaidedsparqlgenerationforsemanticconstructioninformationretrieval |