SWRL Rule Development to Automate Spatial Transactions in Government

Varadharajulu, Premalatha; Arnold, Lesley M.; McMeekin, David A.; West, Geoff; Moncrieff, Simon

doi:10.1007/978-3-319-62618-5_8

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Research on the feasibility of the Semantic Web for spatial infrastructures has been ongoing and continues to be of interest at FrontierSI (https://front iersi.com.au/) (formerly Corporate Research Centre for Spatial Information). This research focused on the viability of Semantic Web Technologies for improving the search and discovery of spatial resources (Reed et al, 2016), orchestration and federation of existing SDI resources (Siao Him Fa, 2018;Tan, McMeekin, West, & Moncrieff, 2017), conflict resolution in spatial transactions and data conflation (Varadharajulu, Arnold, McMeekin, West, & Moncrieff, 2017;Yu, McMeekin, Arnold, & West, 2018), production quality management (Ivánová, Armstrong, & McMeekin, 2017;Ziaimatin, Nili, Barros, Ivánová, & Spencer, 2018), and the inclusion of the community of volunteered spatial data producers (Goodhue, McNair, & Reitsma, 2015;McNair & Arnold, 2016).…”

Section: Sdis Their Evolution and Limitationsmentioning

confidence: 99%

From spatial data to spatial knowledge infrastructure: A proposed architecture

Ivánová

McMeekin

et al. 2020

Transactions in GIS

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Spatial data infrastructures (SDIs) provide access to spatial data and services for humans to solve spatial problems but represent a barrier for machines such as search engines or spatial services. In this article we propose an architecture for spatial knowledge infrastructure (SKI), which attempts to overcome these limitations, and describe the interactions between its components. The SKI architecture proposed is illustrated for two scenarios. The first is an architecture for an ideal solution requiring spatial data on the web, and the second is for a practical short‐term solution that takes into consideration legacy SDIs as well as spatial data on the web in its implementation. In addition, the utility of the proposed SKI architecture is illustrated with two examples demonstrating the scope of the SKI in two different ways: where the SKI is a “knowledge enabler,” and where the SKI acts as a “knowledge creator.” A discussion on how well the proposed architecture meets current best practice for spatial data on the web, as well as obstacles and challenges in the implementation of the proposed solutions, concludes the article.

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Section: Sdis Their Evolution and Limitationsmentioning

confidence: 99%

From spatial data to spatial knowledge infrastructure: A proposed architecture

Ivánová

McMeekin

et al. 2020

Transactions in GIS

Self Cite

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“…In addition to quality management with OWL, SWRL rules are used in several studies for quality assessment process. (Wang et al, 2005); (Cheng et al, 2008); (Keßler et al, 2009); (Zhu, 2013); (Cherfi et al, 2017); (Varadharajulu et al, 2017); (Mobasheri, 2017); (Homburg and Boochs, 2019) can be given as example. Wang et al (2005) developed a system to detect inconsistent spatial data with the help of SWRL rules in a specific domain.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al (2005) developed a system to detect inconsistent spatial data with the help of SWRL rules in a specific domain. Homburg and Boochs (2019), Varadharajulu et al (2017), and Mobasheri (2017) propose rule-based approaches including SWRL rules for domain dependent solutions. Varadharajulu et al (2017) design a framework to check the consistency of the transportation data against the rules that are created with SWRL.…”

Section: Introductionmentioning

confidence: 99%

Sdqo and Sfo, Ontologies for Spatial Data Quality Assessment

Yılmaz¹,

Cömert²,

Yıldırım³

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Spatial quality assessment is based on the conformance of data to its specifications or fitness for users' purpose. These specifications and the users' purposes include the rules and constraints that a dataset should comply with. Assessing the compliance of data to the rules is still an active research subject and rule-based approach is the common method. For the efficient rule-based system implementation, it is desired to automate assessment process with a domain-independent and web-based approach. Reasoning capability and re-usability of semantic web components are expected to promote efficient implementation. In literature, many domains such as agriculture, music, Linked Data and geospatial domain etc. apply ontology-based methods for quality management. There is a need to model geospatial quality concepts and rules in a domain-independent way to automate the quality management process. In our model of rule formalism, we use Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). We devise two types of ontologies. These are; the specification ontologies (SfO) and the Spatial Data Quality Ontology (SDQO). SfO is to be created by domain experts/users to define rules according to specifications. SDQO is responsible with quality assessment; it is domain independent and makes assessment based on the rules defined by any SfO for the related domain. The quality elements are domain and toposemantic consistency that assessed by SWRL. In this paper, the design considerations of the ontologies for quality assessment are explained with an example.

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“…As the ontology classification is built on simple triples, an ontology can be enhanced with the use of the Semantic Web Rule Language (SWRL) (Horrocks et al, 2004). An ontology for the road network has been developed by Varadharajulu et al (2016) with the application of an automated decision making processes for spatially related transactions, such as the verification of a valid road name (whereby a road name of a new road cannot be used again within 10 km in city areas and 50 km in rural areas).…”

Section: Introductionmentioning

confidence: 99%

Introducing a Framework for Conflating Road Network Data With Semantic Web Technologies

Niestroj

McMeekin

Helmholz

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Road network asset management is a challenging task as many data sources with different road asset location accuracies are available. In Australia and New Zealand transport agencies are investigating into harmonisation of road asset data, whereby two or more data sets are merged to create a new data set. Currently, identifying relations between road assets of the same meaning is not always possible, as road authorities of these countries use their own data structures and standards. This paper employs SemanticWeb Technologies, such as RDF/Turtle ontologies and semantic rules to enable road network conflation (merge multiple data sets without creating a new data set) as a first step towards data harmonisation by means of information exchange, and shifts road network data from intersections and road nodes to data sets considering the accuracy of the data sets in the selected area. The data integration from GeoJSON into RDF/Turtle files is processed with Python. A geographic coordinates shifting algorithm reads unique data entries that have been extracted from RDF/Turtle into JSON-LD and saves the processed data in their origin file format, so that a closed data flow can be approached.

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SWRL Rule Development to Automate Spatial Transactions in Government

Cited by 4 publications

References 11 publications

From spatial data to spatial knowledge infrastructure: A proposed architecture

From spatial data to spatial knowledge infrastructure: A proposed architecture

Sdqo and Sfo, Ontologies for Spatial Data Quality Assessment

Introducing a Framework for Conflating Road Network Data With Semantic Web Technologies

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