Semantic Web Technologies Automate Geospatial Data Conflation: Conflating Points of Interest Data for Emergency Response Services

Fu-wei, YU; McMeekin, David A.; Arnold, Lesley M.; West, Geoff

doi:10.1007/978-3-319-71470-7_6

Cited by 13 publications

(13 citation statements)

References 18 publications

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“…Comparing OWL and SWRL techniques based on image processing decisions is conducted by Gu et al (2017) showing that minor improvements (about 1%) in land cover classification can be achieved by comparing both techniques. The evaluation of a Semantic Web map conflation POI approach is guided by Yu et al (2018) with the high accuracy of 98% in POI identification for shopping centres which are included in all data sets.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the ontology and semantic rules editor Protégé will be used. Both tools are Open Source and used by other researchers, such as Yu et al (2018).…”

Section: Stage 1: Identify An Improved Data Specification and Establimentioning

confidence: 99%

“…Rubber-sheeting creates a new data set with adjusted polylines containing metadata information of conflated data sets (Haunert, 2005). Yu et al (2018) uses Semantic Web Technologies to utilise a Points of Interest (POI) approach to conflate POI data sets from Landgate, Western Australian Police and the Department of Fire and Emergency services into one new POI data set. This study proposes a new strategy to analyse, match and conflate road asset metadata information from multiple data sources such as Main Roads Western Australia (MRWA), Western Australian Land Information Authority (Landgate), as well as Open-StreetMap (OSM) and enable both harmonised new data and federated query data.…”

Section: Introductionmentioning

confidence: 99%

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A Proposal to Use Semantic Web Technologies for Improved Road Network Information Exchange

Niestroj

McMeekin

Helmholz

et al. 2018

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

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<p><strong>Abstract.</strong> Data harmonisation improves the coherence between data sets within and across themes and is, therefore, a very helpful tool for governmental agencies, companies and other organisations that share their data. This research focuses on horizontal infrastructures, namely roads, and proposes a new strategy to apply Semantic Web Technologies. The aim is to understand if their application is efficient and effective in filling the gap of data harmonisation in Australia’s and New Zealand’s road asset management systems within the definition of location. The proposed strategy has three stages. First, available international data standards for road assets will be analysed to identify the gaps within these standards and create recommendations towards an improved standard. The second stage is for the location aspect within each stage of the life cycle of asset management with respect to existing road asset data standards. Finally, in a third stage Semantic Web Technologies, ontologies and semantic rules will be used to build a prototype solution for road asset data conflation by merging multiple data sources that share no common lineage. The application of these technologies will allow for easier search and discovery of this data as well as facilitate the automated processing and updating of this data over the Web.</p>

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Section: Discussionmentioning

confidence: 99%

“…In addition, the ontology and semantic rules editor Protégé will be used. Both tools are Open Source and used by other researchers, such as Yu et al (2018).…”

Section: Stage 1: Identify An Improved Data Specification and Establimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Proposal to Use Semantic Web Technologies for Improved Road Network Information Exchange

Niestroj

McMeekin

Helmholz

et al. 2018

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

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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%

“…However, with exposing SKI metadata in RDFa format (e.g., through GeoDCAT-AP), these resources are "visible" to smart applications utilizing Semantic Web tools. In our earlier research (Yu et al, 2018), we examined the automatic conflation of several heterogamous points of interest datasets covering the same region. The user application in the prototype (an equivalent of ES UserApp in Figure 1) applies a Semantic Web Rule Language (SWRL) rule-based method for modeling user analytic logic and reasoning steps to automate spatial data conflation, in which it utilizes information about data provenance, topological relationships, business policies, and other metadata to integrate several POIs to determine an unambiguous/exact location.…”

Section: Ski As Enabler Of Knowledge On-demandmentioning

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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