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Reseach Article

Ontology Employment in Text Document Clustering combined with Grouping Algorithm

by Hmway Hmway Tar, Pye Phyo Oo
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 6 - Number 3
Year of Publication: 2013
Authors: Hmway Hmway Tar, Pye Phyo Oo
10.5120/ijais13-451026

Hmway Hmway Tar, Pye Phyo Oo . Ontology Employment in Text Document Clustering combined with Grouping Algorithm. International Journal of Applied Information Systems. 6, 3 ( October 2013), 11-14. DOI=10.5120/ijais13-451026

@article{ 10.5120/ijais13-451026,
author = { Hmway Hmway Tar, Pye Phyo Oo },
title = { Ontology Employment in Text Document Clustering combined with Grouping Algorithm },
journal = { International Journal of Applied Information Systems },
issue_date = { October 2013 },
volume = { 6 },
number = { 3 },
month = { October },
year = { 2013 },
issn = { 2249-0868 },
pages = { 11-14 },
numpages = {9},
url = { https://www.ijais.org/archives/volume6/number3/534-1026/ },
doi = { 10.5120/ijais13-451026 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:52:24.236754+05:30
%A Hmway Hmway Tar
%A Pye Phyo Oo
%T Ontology Employment in Text Document Clustering combined with Grouping Algorithm
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 6
%N 3
%P 11-14
%D 2013
%I Foundation of Computer Science (FCS), NY, 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.

References
  1. M. Jursic , N. Lavrac, "Fuzzy Clustering Of Documents", Department of Knowledge Discovery.
  2. M. SteinBach, G. Karypis and V. Kumar, "A Comparison of Document Clustering Techniques" in KDD Workshop on Text Mining, 2000.
  3. M-L. Reinberger, P. Spyns, "Discovering Knowledge in Texts for the Learning of DOGMA-inspired ontologies". In Proceedings of ECAI 2004 Workshop on Ontology Learning and Population, 2004.
  4. N. F. Noy and D. L. McGuiness. "Ontology Development 101: A Guide to Creating Your First Ontology", Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, March 2001.
  5. S. Bechhofer, I. Horrocks, C. Goble, R. Stevens, " OILEd: A Reasonable Ontology Editor for the Semantic Web" , In KI2001, Joint German/Austrian conference on Artificial Intelligence, volume LNAI Vol. 2174, pages 396-408, Vienna ,2001.
  6. S. Karapiperis and D. Apostolou, "Consensus Building in Collaborative Ontology Engineering Processes", Journal of Universal Knowledge Management, 1(3), 199-216, 2006
  7. T. Berners-Lee, "Weaving the Web", Harper, San Francisco, 1999, HarperCollins Publishers, New York, NY, 1999.
  8. H. H. Tar, T. T. S. Nyunt, "Ontology-based Concept Weighting for Text Documents", World Academy of Science, Engineering and Technology.
  9. Yllias Chali and Sou?ane Noureddine, "Document Clustering with Grouping and Chaining Algorithms", University of Lethbridge.
Index Terms

Computer Science
Information Sciences

Keywords

Semantic Web Clustering Text Clustering Algorithm