知识抽取
知识库
计算机科学
决策支持系统
RDF公司
资源(消歧)
基于知识的系统
云计算
数据科学
数据挖掘
知识管理
语义网
人工智能
计算机网络
操作系统
作者
Ritaban Dutta,Ahsan Morshed,Jagannath Aryal,Claire D’Este,Aruneema Das
标识
DOI:10.1016/j.envsoft.2013.10.004
摘要
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.
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