Improving data management through automatic information extraction model in ontology for road asset management
lRoads are a critical component of transportation infrastructure, and their effective maintenance is paramount in ensuring their continued functionality and safety. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The appro...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/95544 |
| _version_ | 1848766022984663040 |
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| author | Lei, Xiang |
| author_facet | Lei, Xiang |
| author_sort | Lei, Xiang |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | lRoads are a critical component of transportation infrastructure, and their effective maintenance is paramount in ensuring their continued functionality and safety. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success road management projects, on the other hand improves information management practices in the construction industry. |
| first_indexed | 2025-11-14T11:44:33Z |
| format | Thesis |
| id | curtin-20.500.11937-95544 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:44:33Z |
| publishDate | 2023 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-955442024-07-22T04:11:46Z Improving data management through automatic information extraction model in ontology for road asset management Lei, Xiang lRoads are a critical component of transportation infrastructure, and their effective maintenance is paramount in ensuring their continued functionality and safety. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success road management projects, on the other hand improves information management practices in the construction industry. 2023 Thesis http://hdl.handle.net/20.500.11937/95544 Curtin University fulltext |
| spellingShingle | Lei, Xiang Improving data management through automatic information extraction model in ontology for road asset management |
| title | Improving data management through automatic information extraction model in ontology for road asset management |
| title_full | Improving data management through automatic information extraction model in ontology for road asset management |
| title_fullStr | Improving data management through automatic information extraction model in ontology for road asset management |
| title_full_unstemmed | Improving data management through automatic information extraction model in ontology for road asset management |
| title_short | Improving data management through automatic information extraction model in ontology for road asset management |
| title_sort | improving data management through automatic information extraction model in ontology for road asset management |
| url | http://hdl.handle.net/20.500.11937/95544 |