An Investigation on Ontology Based Fuzzy Semantic Information Retrieval
DOI:
https://doi.org/10.61841/a08sdf97Keywords:
Fuzzy Logic, Fuzzy Ontology, Information Retrieval, Ontology Engineering, Semantic Web.Abstract
Information retrieval, a core research area, has caught the minds of many computer science researchers. The research survey is an amalgamation of various findings done in the effectiveness of ontology-based information retrieval with fuzzy logic content evaluation. The inference of the scan with many papers widens the understanding of a researcher on fuzzy ontology-based information retrieval, which has come by converting an ordinary ontology to a fuzzy ontology. The paper gives an explanatory note on several models used in information retrieval. Comprehensive comparative studies on algorithms in the areas revolving ontology with information retrieval are also prescribed. A framework on the existing OBIR (ontology-based information retrieval) being developed for education could be the next nearest work following the literature review, which remains the conclusion of the proposed work.
Downloads
References
[1] Y. Sure, S. Staab and S. Studer, “Ontology Engineering Methodology”, Springer, pp. 135-152, May 2009.
[2] S. Calegari, D. Ciucci, “Towards a Fuzzy Ontology Definition and a Fuzzy Extension of an Ontology Editor”,
ICEIS 2006, Vol. 3, pp. 147-158, May 2006.
[3] P. Alexopoulos, M. Wallace, “Improving Automatic Semantic Tag Recommendation through Fuzzy Ontologies”,
IEEE Press, December 2012.
[4] P. Alexopoulos, M. Wallace, K. Kafentzis, D. Askounis, “IKARUS-Onto: a methodology to develop fuzzy
ontologies from crisp ones”, Knowledge and Information Systems, Springer, Vol. 32, pp. 667-695, November
2011.
[5] J. Hu, Xinzhou. Lu, C.Guan, “A Semantic Information Retrieval Approach Based on Rough Ontology”, The
Open Cybernetics and Systemics Journal, Vol. 8, pp. 399-404, December 2014.
[6] S. Kumar, R.K. Rana, and P. Singh, "Ontology-based Semantic Indexing Approach for Information Retrieval
System”, International Journal of Computer Applications, Vol. 49, July 2012.
[7] S. Meenakshi, R.M. Suresh, “A Study and analysis on Web Information Retrieval System for Distributed
Environment”, International Journal of Applied Engineering Research, Vol. 11, pp. 2165-2176, 2016.
[8] M. Baziz, M. Boughanem, Y. Loiseau, H. Prade, “Fuzzy Logicand Ontology-based Information Retrieval”, Fuzzy
Logic, Vol. 215, pp. 19 3-2 18, 20 07.
[9] H.B. Styltsvig, "Ontology-based Information Retrieval”, A dissertation for the Degree of Doctor of Philosophy,
Roskilde University, May 2006.
[10] R.S. Khokale, M. Atique, "Web-Based Information Retrieval using Fuzzy Logic”, JSCSE 3, Vol. 3, pp. 62-68,
March 2013.
[11] Y. Gupta, A. Saini, and A.K. Saxena, “A new similarity function for information retrieval based on fuzzy logic”,
International Conference on Advances in Computing, Communications and Informatics, IEEE Press, September
2014.
[12] J. Singh,A.Sharan,“A new fuzzy logic-based query expansion model for efficient information retrieval using
relevance feedback approach”, Neural Computing and Applications, Springer, pp. 1–24, February 2016.
[13] Y. Gupta, A. Saini, and A.K. Saxena, “A new fuzzy logic-based ranking function for efficient Information Retrieval
system”, Expert Systems with Applications. 42, pp. 1223–1234, February 2015.
[14] M. Hourali, G.M. Montazer, “An Intelligent Information Retrieval Approach Based on Two Degrees of
Uncertainty Fuzzy Ontology”, Advances in Fuzzy Systems. 7, Vol. 2011, August 2011.
[15] K. Balasubramaniam, “Hybrid Fuzzy-ontology Design Using FCA Based Clustering for Information Retrieval in
Semantic Web”, 2nd International Symposium on Big Data and Cloud Computing, Procedia Computer Science.
50, Vol. 50, pp. 135-142, May 2015.
[16] Z.E. Attia, A.M. Gadallah, and H.M. Hefny, “Semantic Information Retrieval Model: Fuzzy Ontology Approach”,
International Journal of Computer Applications 13, Vol. 91, April 2014.
[17] G. Nagarajan, R.I. Minu, “Fuzzy Ontology-Based Multimodal Semantic Information Retrieval”, International
Conference on Intelligent Computing, Communication & Convergence, Procedia Computer Science 48, pp. 101
106, May 2015.
[18] J. Liu et al., “Ontology representation and mapping of common fuzzy knowledge”, Neurocomputing, Vol. 215,
Pp. 184-195, November 2016.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Author
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
