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SWRL Rule Development to Automate Spatial Transactions in Government

  • Premalatha VaradharajuluEmail author
  • Lesley Arnold
  • David A. McMeekin
  • Geoff West
  • Simon Moncrieff
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 741)

Abstract

The land development approval process between local councils and government planning authorities is time consuming and resource intensive because human decision-making is required to complete a transaction. This is particularly apparent when seeking approval for a new land subdivisions and administrative boundary changes that require changes to spatial datasets. This paper presents a methodology that automates the approval process by developing. Feedback on the transaction is communicated to the land developer in real-time, thus reducing process handling time for both developer and the government agency. This paper presents an approach for knowledge acquisition on rule development using Semantic Web and Artificial Intelligence to automate the spatial transaction process. The Web Ontology Language (OWL) is used to represent relationships between different entities in the spatial database schema. Rules that replicate human knowledge are extracted from government policy documents and subject-matter experts, and are defined in the form of Semantic Web Rule Language (SWRL) and based on geometry and attributes of database entities. The SWRL rules work with OWL-2 (spatial schema and vocabulary) ontologies to enable the automatic transactions to occur. These rules are implemented using an ontology and rule reasoner, which accesses the instances of data elements stored in the underlying spatial database. When the developer submits an application, the software checks the rules against the request for compliance with the relevant government policies and standards. This paper presents results for dealing with road proposals and road name approvals.

Keywords

Spatial transaction Spatial data supply chain Artificial intelligence Semantic Web Ontology Rule-based reasoning OWL-2 

Notes

Acknowledgement

The work has been supported by the Cooperative Research Centre for Spatial Information, whose activities are funded by the Australian Commonwealth’s Cooperative Research Centres Programme. The authors extend their thanks to Landgate for providing the example datasets for the case study and subject matter experts for rule formulation.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Premalatha Varadharajulu
    • 1
    • 2
    Email author
  • Lesley Arnold
    • 1
    • 2
  • David A. McMeekin
    • 1
    • 2
  • Geoff West
    • 1
    • 2
  • Simon Moncrieff
    • 1
    • 2
  1. 1.Curtin UniversityPerthAustralia
  2. 2.Cooperative Research Centre for Spatial InformationCarltonAustralia

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