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Recommending environmental knowledge as linked open data cloud using semantic machine learning


Morshed, A and Dutta, R and Aryal, J, Recommending environmental knowledge as linked open data cloud using semantic machine learning, Workshops Proceedings of the 29th IEEE International Conference on Data Engineering, 8-11 April 2013, Brisbane, Australia, pp. 27-28. ISSN 1084-4627 (2013) [Refereed Conference Paper]

Copyright Statement

Copyright 2013 IEEE

DOI: doi:10.1109/ICDEW.2013.6547421


Large scale environmental knowledge integration and development of a knowledge recommendation system for the Linked Open Data Cloud using semantic machine learning approach was the main mission of this research. This study considered five different environmental big data sources including SILO, AWAP, ASRIS, MODIS and CosmOz complementary for knowledge integration. Unsupervised clustering techniques based on principal component analysis (PCA) and Fuzzy-C-Means (FCM) and Self-organizing map (SOM) clustering was used to learn the extracted features and to create a 2D map based dynamic knowledge recommendation system. Knowledge was stored in a triplestore using triples format (subject, predicate, and object) along with the complete meta-data provenance information. The Resource Description Framework (RDF) representation made i-EKbase very flexible to integrate with the Linked Open Data (LOD) cloud. The developed Intelligent Environmental Knowledgebase (i-EKbase) could be used for any environmental decision support application.

Item Details

Item Type:Refereed Conference Paper
Keywords:RDF, triplestore, PCA, FCM, g-SOM based visual selection, 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)
ID Code:86605
Year Published:2013
Deposited By:Geography and Environmental Studies
Deposited On:2013-10-01
Last Modified:2017-10-30

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