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    Ontology-Based Information Integration: A State-of-the-Art Review in Road Asset Management

    Access Status
    Fulltext not available
    Authors
    Lei, X.
    Wu, Peng
    Zhu, Junxiang
    Wang, J.
    Date
    2021
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Lei, X. and Wu, P. and Zhu, J. and Wang, J. 2021. Ontology-Based Information Integration: A State-of-the-Art Review in Road Asset Management. Archives of Computational Methods in Engineering.
    Source Title
    Archives of Computational Methods in Engineering
    DOI
    10.1007/s11831-021-09668-6
    ISSN
    1134-3060
    Faculty
    Faculty of Humanities
    School
    School of Design and the Built Environment
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP180104026
    URI
    http://hdl.handle.net/20.500.11937/87787
    Collection
    • Curtin Research Publications
    Abstract

    As a novel and efficient method of knowledge management, ontology provides a machine-processable technique to establish structured knowledge/information for effective management. The advantages, disadvantages, and future directions of ontology in road asset management, which relies heavily on acquiring and using data, are attracting much research attention over the past few years. This paper aims to provide a thorough and systematic review of ontology, including its development and implementation, in road asset management. In total, 45 journal papers and 12 conference papers published over the last 14 years were reviewed, sorted, and analysed. It is observed that: (1) most ontologies in road asset management target at traffic service and road assets; (2) most ontologies are designed to support the operation and maintenance stage; and (3) RDF-based language and OWL semantics are the two most popular ontology technique. From the review, it is found that the current development and implementation of ontology in road asset management also have a few limitations, including the lack of specific ontology engineering approach, the lack of an automatic mechanism to capture instances, properties and relationships, limited ontologies techniques in this field, and the lack of sharing and linking ontologies of different domains. This study provides useful reference for the architecture, engineering and construction industry to understand and select the most appropriate ontology techniques for creating structured knowledge bases and making effective knowledge management decisions.

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