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http://dx.doi.org/10.5392/JKCA.2017.17.11.389

Semantic Clustering Model for Analytical Classification of Documents in Cloud Environment  

Kim, Young Soo (배재대학교 사이버보안학과)
Lee, Byoung Yup (배재대학교 사이버보안학과)
Publication Information
The Journal of the Korea Contents Association / v.17, no.11, 2017 , pp. 389-397 More about this Journal
Abstract
Recently semantic web document is produced and added in repository in a cloud computing environment and requires an intelligent semantic agent for analytical classification of documents and information retrieval. The traditional methods of information retrieval uses keyword for query and delivers a document list returned by the search. Users carry a heavy workload for examination of contents because a former method of the information retrieval don't provide a lot of semantic similarity information. To solve these problems, we suggest a key word frequency and concept matching based semantic clustering model using hadoop and NoSQL to improve classification accuracy of the similarity. Implementation of our suggested technique in a cloud computing environment offers the ability to classify and discover similar document with improved accuracy of the classification. This suggested model is expected to be use in the semantic web retrieval system construction that can make it more flexible in retrieving proper document.
Keywords
Cloud; Semantic; Keyword Frequency; Concept Matching; Clustering;
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Times Cited By KSCI : 5  (Citation Analysis)
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