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-CMeans (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.

Item Type:	Refereed Article
Keywords:	feature, semantic matching, knowledge integration, i-EKbase, linked open data cloud
Research Division:	Engineering
Research Group:	Geomatic engineering
Research Field:	Photogrammetry and remote sensing
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in the environmental sciences
UTAS Author:	Aryal, J (Dr Jagannath Aryal)
UTAS Author:	Das, A (Dr Aruneema Das)
ID Code:	87641
Year Published:	2014
Web of Science® Times Cited:	32
Deposited By:	Geography and Environmental Studies
Deposited On:	2013-11-28
Last Modified:	2017-10-25
Downloads:	0