Semantic Sensor Networks

The Australian Bureau of Meteorology (BOM) has recently published a homogenised daily temperature dataset, ACORN-SAT, for the monitoring of climate variability and change in Australia. The dataset employs the latest analysis techniques and takes advantage of newly digitised observational data to provide a daily temperature record over the last 100 years. In this paper, we present a case-study to publish the ACORN-SAT as Linked Data. We use the Semantic Sensor Network ontology to deliver the publicly available metadata about the BOM weather stations and their deployment history as linked data. Additionally, for concepts that are not covered by existing vocabularies, we have developed domain ontologies to define the adjusted aggregate variables and associated parameters for the ACORN-SAT homogenised observation data, the BOM weather stations and the BOM Rainfall districts. We use the RDF Data Cube Vocabulary to publish the originally released tabular time series data and structure it into slices to support multiple views and query endpoints. We further describe how these linked open vocabularies have been used and combined in the context of this project to make this dataset linkable to existing or future linked open data resources. We also discuss the versatility of the new service for the consumers of the ACORNSAT dataset and uncover some issues which are specific to such long term climate data time series. The resulting Linked Sensor Data Cube is now accessible online via a pilot government linked data service built on the Linked Data API at lab.environment.data.gov.au.