Environmental Spatio-temporal Ontology for the Linked Open Data Cloud

The rapid access of sensor technology provides both challenges and opportunities to authenticated spatiotemporal data. Authentication can be assured by developing related ontologies. Ontology explicitly specifies shared conceptualization and formal vocabularies. In this paper, we proposed an environmental spatio-temporal ontology (ESTO) using unified resource description framework (RDF) and Intelligent Environmental Knowledgebase (i-EKbase) recommendation system. Five different environmental data sources namely SILO, AWAP, ASRIS, CosmOz, and MODIS were considered to develop i-EKbase where knowledge was integrated. The recommendation system was founded on web based large scale dynamic data mining, contextual knowledge extraction, and integrated knowledge representation. The proposed ESTO was tested for optimization of the accessibility and usability issues related to big data sets and minimize the overall application costs. RDF representation made this ontology very flexible to publish on Linked Open Data Cloud environment.