A Semi-Automatic Ontology Development Framework for Knowledge Transformation of Construction Safety Requirements
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| Publicat a: | Buildings vol. 15, no. 4 (2025), p. 569 |
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| Autor principal: | |
| Altres autors: | , |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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|---|---|---|---|
| 001 | 3170902956 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2075-5309 | ||
| 024 | 7 | |a 10.3390/buildings15040569 |2 doi | |
| 035 | |a 3170902956 | ||
| 045 | 2 | |b d20250215 |b d20250228 | |
| 084 | |a 231437 |2 nlm | ||
| 100 | 1 | |a Wu, Zhijiang |u College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225009, China | |
| 245 | 1 | |a A Semi-Automatic Ontology Development Framework for Knowledge Transformation of Construction Safety Requirements | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Construction safety requirements (SRs), which serve as critical information encapsulating a wide range of safety-related issues, constitute a fundamental basis for effective construction safety management. The constraints of the complex information characteristics and uncertainty of knowledge migration, however, lead to the failure to transform most of the requirement information into effective knowledge. This study proposes a multi-stage knowledge transformation framework for realizing the transformation of SRs from abstract information to canonical knowledge, and it accurately completes the knowledge transformation through document matching, knowledge extraction, and knowledge representation. Meanwhile, a semi-automated model was introduced into this study to develop a domain ontology knowledge base for SRs and to represent each type of knowledge through class definitions. The proposed framework was validated by testing project documents collected from two types of building projects, and the results show that the RD-based association rules can accurately match documents associated with SRs and adapt to match different types of sentiment attribute documents. Moreover, the improved TF-IDF algorithm improved by 20% in precision and recall, showing that the algorithm can extract tacit knowledge by combining knowledge points. Further, the domain ontology knowledge base facilitates normative documentation and representation for each type of knowledge in SRs. | |
| 653 | |a Construction accidents & safety | ||
| 653 | |a Knowledge bases (artificial intelligence) | ||
| 653 | |a Algorithms | ||
| 653 | |a Ontology | ||
| 653 | |a Tacit knowledge | ||
| 653 | |a Safety | ||
| 653 | |a Knowledge management | ||
| 653 | |a Explicit knowledge | ||
| 653 | |a Automation | ||
| 653 | |a Project engineering | ||
| 653 | |a Knowledge representation | ||
| 653 | |a Transformations (mathematics) | ||
| 653 | |a Construction industry | ||
| 653 | |a Safety management | ||
| 653 | |a Occupational safety | ||
| 653 | |a Decision making | ||
| 653 | |a Documents | ||
| 653 | |a Project management | ||
| 653 | |a Safety standards | ||
| 653 | |a Nuclear power plants | ||
| 653 | |a Semantics | ||
| 700 | 1 | |a Liu, Mengyao |u School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang 330013, China | |
| 700 | 1 | |a Ma, Guofeng |u Department of Construction Management and Real Estate, School of Economics and Management, Tongji University, Shanghai 200092, China; <email>guofengma@tongji.edu.cn</email> | |
| 773 | 0 | |t Buildings |g vol. 15, no. 4 (2025), p. 569 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3170902956/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3170902956/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3170902956/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |