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Recommending environmental knowledge as linked open data cloud using semantic machine learning
conference contribution
posted on 2023-05-23, 07:54 authored by Morshed, A, Dutta, R, Jagannath AryalLarge 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.
History
Publication title
Workshops Proceedings of the 29th IEEE International Conference on Data EngineeringEditors
CY Chan, J Lu, K Norvag, E TaninPagination
27-28ISSN
1084-4627Department/School
School of Geography, Planning and Spatial SciencesPublisher
ICDE CommitteePlace of publication
AustraliaEvent title
29th IEEE International Conference on Data EngineeringEvent Venue
Brisbane, AustraliaDate of Event (Start Date)
2013-04-08Date of Event (End Date)
2013-04-11Rights statement
Copyright 2013 IEEERepository Status
- Restricted