Improving data management through automatic information extraction model in ontology for road asset management
dc.contributor.author | Lei, Xiang | |
dc.contributor.supervisor | Peng Wu | en_US |
dc.date.accessioned | 2024-07-22T04:11:46Z | |
dc.date.available | 2024-07-22T04:11:46Z | |
dc.date.issued | 2023 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/95544 | |
dc.description.abstract |
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. | en_US |
dc.publisher | Curtin University | en_US |
dc.title | Improving data management through automatic information extraction model in ontology for road asset management | en_US |
dc.type | Thesis | en_US |
dcterms.educationLevel | PhD | en_US |
curtin.department | School of Design and the Built Environment | en_US |
curtin.accessStatus | Open access | en_US |
curtin.faculty | Humanities | en_US |
curtin.contributor.orcid | Lei, Xiang [0000-0003-2838-984X] | en_US |