Abstract
role in the analysis and management of RFI documents. However, these metadata are manually entered in the RFI management system, which results in loss of time and incorrect entries. This study aims to demonstrate that metadata of RFI documents can be extracted and assigned automatically using natural language processing and machine learning algorithms. To achieve this aim, the performance of Naïve Bayes and K-Nearest Neighbor algorithms are evaluated and compared. The results show that machine learning models perform well in automatically extracting the metadata of RFIs and, the performance of machine learning models for each label varies. The findings of this study can be used to develop an artificial intelligence based RFI management system by integrating natural language processing and machine learning models into the system.
| Original language | English |
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| Title of host publication | Proceedings of the 2024 European Conference on Computing in Construction |
| Editors | Marijana Srećković, Mohamad Kassem, Ranjith Soman, Athanasios Chassiakos |
| Publisher | European Council on Computing in Construction (EC3) |
| Pages | 206-211 |
| Number of pages | 6 |
| ISBN (Print) | 9789083451305 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | European Conference on Computing in Construction, EC3 2024 - Chania, Greece Duration: 14 Jul 2024 → 17 Jul 2024 |
Publication series
| Name | Proceedings of the European Conference on Computing in Construction |
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| Volume | 2024 |
| ISSN (Electronic) | 2684-1150 |
Conference
| Conference | European Conference on Computing in Construction, EC3 2024 |
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| Country/Territory | Greece |
| City | Chania |
| Period | 14/07/24 → 17/07/24 |
Bibliographical note
Publisher Copyright:© 2024 European Council on Computing in Construction.