INTRODUCING A FRAMEWORK FOR CONFLATING ROAD NETWORK DATA WITH SEMANTIC WEB TECHNOLOGIES
Keywords: Data Conflation, Ontologies, Road Network, Semantic Rules, Semantic Web, Shift Geographic Coordinates
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.