CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Ontology based Information Retrieval for Semi Structure Data using Bagging

by N. Vanjulavalli, A. Kovalan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 15
Year of Publication: 2013
Authors: N. Vanjulavalli, A. Kovalan
10.5120/11469-7077

N. Vanjulavalli, A. Kovalan . Ontology based Information Retrieval for Semi Structure Data using Bagging. International Journal of Computer Applications. 67, 15 ( April 2013), 6-11. DOI=10.5120/11469-7077

@article{ 10.5120/11469-7077,
author = { N. Vanjulavalli, A. Kovalan },
title = { Ontology based Information Retrieval for Semi Structure Data using Bagging },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 15 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number15/11469-7077/ },
doi = { 10.5120/11469-7077 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:24:53.052064+05:30
%A N. Vanjulavalli
%A A. Kovalan
%T Ontology based Information Retrieval for Semi Structure Data using Bagging
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 15
%P 6-11
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Ontologies are concept specifications and relations that have a major part in semantic web applications through provision of shared knowledge about real world objects ensuring reusability/interoperability among varied modules. So a semantic application should first have an ontology quality related query. Information retrieval (IR) is obtaining information resources relevant to an information need from various information resources. IR has changed over time with expansion of the internet and the arrival of modern graphical user interfaces/ mass storage devices. The aims are using ontologies knowledge to match object with queries on a semantic basis. Ontologies use has many challenges focussing on application of machine learning techniques on features extracted from ontologies concepts and Natural Language Processing. This paper focuses on classifying universities web pages through use of features extracted from an ontology based semantic interpretation.

References
  1. Stojanovic, N. , Studer, R. , &Stojanovic, L. (2004, September). An approach for step-by-step query refinement in the ontology-based information retrieval. In Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence (pp. 36-43). IEEE Computer Society.
  2. T. R. Gruber, Towards principles for the design of ontologies used for knowledge sharing, in International Journal of Human-Computer Studies, Volume 43, Number 5-6, pp. 907-928, 1995.
  3. N. Guarino, P. Giaretta, Ontologies and Knowledge Bases: Towards a Terminological Clarification, In N. Mars (Eds. ): Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing, IOS Press, pp. 25-32, 1995.
  4. T. R. Gruber, A translation approach to portable ontology specifications, Knowledge Acquisition 5(2) (1993) 199-220.
  5. R. Studer, V. R. Benjamins and D. Fensel, Knowledge Engineering: Principles and methods, Data Knowledge Engineering 25(1) (1998) 161-197.
  6. Wimalasuriya, D. C. , & Dou, D. (2010). Ontology-based information extraction: An introduction and a survey of current approaches. Journal of Information Science, 36(3), 306-323.
  7. He, J. S. K. T. G. , &Naughton, C. Z. D. D. J. (2008). Relational databases for querying XML documents: Limitations and opportunities. 20. 453J / 2. 771J / HST. 958J Biomedical Information Technology Fall 2008.
  8. Koopman, B. , Bruza, P. , Sitbon, L. , &Lawley, M. (2012). Towards semantic search and inference in electronic medical records: an approach using concept-based information retrieval. Australasian Medical Journal.
  9. Haofen Wang, Kang Zhang, Qiaoling Liu, Thanh Tran, and Yong Yu. Q2semantic: A lightweight keyword interface to semantic search. In ESWC, pages 584–598, 2008.
  10. Paralic, J. , &Kostial, I. (2003). Ontology-based information retrieval. In Proceedings of the 14th International Conference on Information and Intelligent systems (IIS 2003), Varazdin, Croatia (pp. 23-28).
  11. Egozi, O. , Markovitch, S. , &Gabrilovich, E. (2011). Concept-based information retrieval using explicit semantic analysis. ACM Transactions on Information Systems (TOIS), 29(2), 8.
  12. Reymonet, A. , Thomas, J. , &Aussenac-Gilles, N. (2009, June). Ontology Based Information Retrieval: an application to automotive diagnosis. In International Workshop on Principles of Diagnosis (DX 2009) (pp. 9-14). Linköping University, Institute of Technology.
  13. Nigam, K. , McCallum, A. K. , Thrun, S. , & Mitchell, T. (2000). Text classification from labelled and unlabeled documents using EM. Machine learning, 39(2), 103-134.
  14. Jones, K. S. (1973). Index term weighting. Information storage and retrieval, 9(11), 619-633.
  15. Salton, G. , & McGill, M. J. (1986). Introduction to modern information retrieval.
  16. Steinbach, M. , Karypis, G. , & Kumar, V. (2000, August). A comparison of document clustering techniques. In KDD workshop on text mining (Vol. 400, pp. 525-526).
  17. J. Bertin, 1977. La graphiqueet le traitementgraphique de l'information, Flammarion, Paris.
  18. Doan, A. , Madhavan, J. , Domingos, P. , & Halevy, A. (2004). Ontology matching: A machine learning approach. Handbook on Ontologies in Information Systems, 397-416.
  19. Breiman, L. (1996b). Bias, variance, and arcing classifiers. Tech. rep. 460, Department of Statistics, University of California, Berkeley, CA.
  20. Bauer, E. , &Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36 (1/2), 105{139.
  21. Maclin, R. , &Opitz, D. (1997). An empirical evaluation of bagging and boosting. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, pp. 546{551 Cambridge, MA. AAAI Press/MIT Press.
  22. Freund, Y. , &Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proc. 13th International Conference on Machine Learning, pp. 148{146. Morgan Kaufmann.
Index Terms

Computer Science
Information Sciences

Keywords

Bagging Information retrieval (IR) Ontology World Wide Web

Powered by PhDFocusTM