An Ontology Based Text Mining
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2014 by IJETT Journal|
|Year of Publication : 2014|
|Authors : Kuwar Aditya , Bhalekar Arjun , Bade Ankush
Kuwar Aditya , Bhalekar Arjun , Bade Ankush. "An Ontology Based Text Mining", International Journal of Engineering Trends and Technology (IJETT), V10(6),292-296 April 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Research project selection is important task for government and private research agencies. When a large number Of research proposals are received, it is common to group them according to their similarities in research discipline areas. The grouped proposals are then assigned to the appropriate experts for peer review. Current methods for grouping proposals are based on manual matching of similar research discipline areas or keywords. However, the exact research discipline areas of the proposals cannot be determined accurately by the applicants due to their subjective views and possible misinterpretations. Therefore, rich information in the proposals’ full text can be used effectively. Text mining methods have been proposed to solve problem by automatically classifying text documents. This paper presents an ontology based text mining approach to cluster research proposals effectively based on their similarities in research discipline areas. This method can be used to improve the efficiency and effectiveness of research proposal selection processes in government and private research agencies.
 K. Chen and N. Gorla, “Information system project selection using fuzzy logic,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 28, no. 6, pp. 849–855, Nov. 1998.
 L. L. Machacha and P. Bhattacharya, “A fuzzy-logic-based approach to project selection,” IEEE Trans. Eng. Manag., vol. 47, no. 1, pp. 65–73, Feb. 2000.
 J. Butler, D. J. Morrice, and P. W. Mullarkey, “A multiple attribute utility theory approach to ranking and selection,” Manage. Sci., vol. 47, no. 6, pp. 800–816, Jun. 2001.
 Q. Tian, J. Ma, J. Liang, R. Kowk, O. Liu, and Q. Zhang, “An organizational decision support system for effective R&D project selection,” Decis. Support Syst., vol. 39, no. 3, pp. 403–413, May 2005.
 S. Hettich and M. Pazzani, “Mining for proposal reviewers: Lessons learned at the National Science Foundation,” in Proc. 12th Int. Conf. Knowl. Discov. Data Mining, 2006, pp. 862–871.
 Y. Liu, Y. Jiang, and L. Huang, “Modeling complex architectures based on granular computing on ontology,” IEEE Trans. Fuzzy Syst., vol. 18, no. 3, pp. 585–598, Jun. 2010.
 Jian Ma, Wei Xu, Yong-hong Sun, Efraim Turban,Shouyang Wang, and Ou Liu “An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection” IEEE transactions on systems, man, and cybernetics—part a: systems and humans, vol. 42, no. 3, may 2012
Ontology, research project selection, text mining, clustering