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Ontology Employment in Text Document Clustering combined with Grouping Algorithm

Hmway Hmway Tar, Pye Phyo Oo Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2013
© 2012 by IJAIS Journal
10.5120/ijais13-451026
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  1. Hmway Hmway Tar and Pye Phyo Oo. Article: Ontology Employment in Text Document Clustering combined with Grouping Algorithm. International Journal of Applied Information Systems 6(3):11-14, October 2013. BibTeX

    @article{key:article,
    	author = "Hmway Hmway Tar and Pye Phyo Oo",
    	title = "Article: Ontology Employment in Text Document Clustering combined with Grouping Algorithm",
    	journal = "International Journal of Applied Information Systems",
    	year = 2013,
    	volume = 6,
    	number = 3,
    	pages = "11-14",
    	month = "October",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Incorporating semantic knowledge from ontology into text document clustering is an important but challenging problem. Moreover, there are many of computer science and medical based subject related papers and journals cited on the Internet. The purpose of this system is to cluster the documents based upon the statistical method and from the semantic web point of view, the system advances in the field of scientific endeavor. Moreover this system is the advanced and extended version of the paper we have been published before. After time passed the testing data amount becomes lager and lager and we have been found that our previous methods should have to improve in more mathematically. Finally, it also reports on the experiments that performed to test the system utilization weighting scheme which is used to encode the importance of concepts inside documents. For the experiments the system has to use ontology that enables us to describe and organize this from heterogeneous sources, and to cluster about it. The experiments reveal that even the testing documents increased; the system may actually be able to produce useful results.

Reference

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Keywords

Semantic Web, Clustering, Text Clustering Algorithm