Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Creating knowledge out of interlinked data
Semantic Web
Environmental Modelling & Software
Semantic Perception: Converting Sensory Observations to Abstractions
IEEE Internet Computing
A new landscape for distributed and parallel data management
Distributed and Parallel Databases
Sieve: linked data quality assessment and fusion
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Evaluating OpenMI as a model integration platform across disciplines
Environmental Modelling & Software
A fully automated and integrated multi-scale forecasting scheme for emergency preparedness
Environmental Modelling & Software
Spatial model steering, an exploratory approach to uncertainty awareness in land use allocation
Environmental Modelling & Software
Procedural knowledge for integrated modelling: Towards the Modelling Playground
Environmental Modelling & Software
'Integronsters', integral and integrated modeling
Environmental Modelling & Software
Environmental Spatio-temporal Ontology for the Linked Open Data Cloud
TRUSTCOM '13 Proceedings of the 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications
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The purpose of this research was to develop a knowledge recommendation architecture based on unsupervised machine learning and unified resource description framework (RDF) for integrated environmental sensory data sources. In developing this architecture, which is very useful for agricultural decision support systems, we considered web based large-scale dynamic data mining, contextual knowledge extraction, and integrated knowledge representation methods. Five different environmental data sources were considered to develop and test the proposed knowledge recommendation framework called Intelligent Environmental Knowledgebase (i-EKbase); including Bureau of Meteorology SILO, Australian Water Availability Project, Australian Soil Resource Information System, Australian National Cosmic Ray Soil Moisture Monitoring Facility, and NASA's Moderate Resolution Imaging Spectroradiometer. Unsupervised clustering techniques based on Principal Component Analysis (PCA), Fuzzy-C-Means (FCM) and Self-organizing map (SOM) were used to create a 2D colour knowledge map representing the dynamics of the i-EKbase to provide ''prior knowledge'' about the integrated knowledgebase. Prior availability of recommendations from the knowledge base could potentially optimize the accessibility and usability issues related to big data sets and minimize the overall application costs. RDF representation has made i-EKbase flexible enough to publish and integrate on the Linked Open Data cloud. This newly developed system was evaluated as an expert agricultural decision support for sustainable water resource management case study in Australia at Tasmania with promising results.