CFP last date
20 May 2024
Reseach Article

Ontology Driven Approach for Effective Decision Making

by Ashutosh V. Girase, Girish Kumar Patnaik, Sandip S. Patil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 148 - Number 7
Year of Publication: 2016
Authors: Ashutosh V. Girase, Girish Kumar Patnaik, Sandip S. Patil

Ashutosh V. Girase, Girish Kumar Patnaik, Sandip S. Patil . Ontology Driven Approach for Effective Decision Making. International Journal of Computer Applications. 148, 7 ( Aug 2016), 15-21. DOI=10.5120/ijca2016911209

@article{ 10.5120/ijca2016911209,
author = { Ashutosh V. Girase, Girish Kumar Patnaik, Sandip S. Patil },
title = { Ontology Driven Approach for Effective Decision Making },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2016 },
volume = { 148 },
number = { 7 },
month = { Aug },
year = { 2016 },
issn = { 0975-8887 },
pages = { 15-21 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016911209 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:52:42.525898+05:30
%A Ashutosh V. Girase
%A Girish Kumar Patnaik
%A Sandip S. Patil
%T Ontology Driven Approach for Effective Decision Making
%J International Journal of Computer Applications
%@ 0975-8887
%V 148
%N 7
%P 15-21
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Decision-making is the task of every top management in an organization. Decision maker needs relevant and meaningful information to help in taking decisions. Meaningful information retrieval is a challenge for effective decision-making. Due to lack of domain knowledge, meaningful information remains hidden in the database itself. Decisions made out of irrelevant and meaningless information sometimes lead to irreparable damage to organization and its reputation. To retrieve relevant information it is necessary to have background knowledge about the domain. Background knowledge in the form of ontology is an important source of information. Domain ontology used as a source of domain knowledge which retrieves all the meaningful information from the database to help in taking decision. In proposed approach, ontology is used as domain knowledge. Use of ontology improves the relevancy and meaningfulness of the results in order to get more meaningful information for effective decision making. Experimental evaluation shows that, results obtained by using proposed approach are more precise and relevant than existing non-ontological approach.

  1. T. R. Gruber, “Toward principles for the design of ontologies used for knowledge sharing,” International Journal Human Computer Studies, vol. 43, no. 5-6, pp. 907–928, 1995.
  2. W. Xuping, N. Zijian, and C. Haiyan, “Research on association rules mining based-on ontology in ecommerce,” Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing, vol. 2, no. 31, pp. 3542–3549, 2007.
  3. Y. Wang and Y. Chen, “A new association rules mining method based on ontology theory,” Proceedings of the Conference on Advanced Computational Intelligence, vol. 2, no. 6, pp. 287–291, October 2012.
  4. R. Prabowo, M. Jackson, P. Burden, and H.-D. Knoell, “Ontology-based automatic classification for the web pages: Design, implementation and evaluation,” Proceedings of the 3rd International Conference on Web Information Systems Engineering, pp. 182–191, 2002.
  5. P. Sundaramoorthy, M. Sreekrishna, S. Bhuvaneshwari, and M. Selvam, “Ontology based classification of user history in obscured web search,” Proceedings of the 2nd International Conference on Current Trends in Engineering and Technology, pp. 258–261, July 2014.
  6. J. Wen, Z. Li, and X. Hu, “Ontology based clustering for improving genomic ir,” Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems, pp. 225–230, 2007.
  7. A. Hotho, S. Staab, and A. Maedche, “Ontology-based text document clustering,” Springer Science and Business Media, vol. 16, no. 4, pp. 48–54, 2012.
  8. X. Tao, Y. Li, and N. Zhong, “A personalized ontology model for web information gathering,” IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 4, pp. 496–511, April 2011.
  9. D. M. Horacio Saggion, Adam Funk and K. Bontcheva, “Ontology-based information extraction for business intelligence,” Springer Berlin Heidelberg, vol. 4825, pp. 843–856, 2007.
  10. K. Revoredo, J. E. O. Luna, , and F. G. Cozman, “Semantic link prediction through probabilistic description logics,” Journal of the Brazilian Computer Society, pp. 397–409, 2013.
  11. D. Caragea, V. Bahirwani, W. Aljandal, and W. H. Hsu, “Ontology based link prediction in the live journal social network,” Proceedings of the Eighth Symposium on Abstraction, Reformulation, and Approximation, pp. 34–41, 2009.
Index Terms

Computer Science
Information Sciences


Ontology Decision-making Future Prediction Domain knowledge Meaningful information Background knowledge Information retrieval Business intelligence.