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

Abstract

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.