Automatic geospatial data conflation using semantic web technologies

Abstract

Copyright 2016 ACM.A Spatial Data Supply Chain (SDSC) is a series of processes that convert raw spatial data (e.g. road locations) into useable products (e.g. road networks). Duplicate SDSCs are not uncommon across Australia. There exists data duplication between local government authorities, State/Territory government departments and commonwealth agencies. To improve the SDSC process, this research explores data conflation as a means of removing the need for duplicate data handling in SDSCs. The research is studying how Semantic Web Technologies can be used to automate the conflation process. The approach focuses on building ontologies for spatial datasets and creating relevant rules based on geometry, topology, and policies. By satisfying these rules through a computer reasoning process, relevant datasets can be intelligently linked and integrated. The conflated dataset can then be used as the single authoritative and trusted source of data that is fit for multiple purposes. In this way the data can be co-maintained and used by multiple organizations eliminating the need for siloed SDSCs.