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Applications of Semantic Similarity Metrics

Suraiya Parveen1 , Ranjit Bisswas2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-11 , Page no. 413-416, Nov-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i11.413416

Online published on Nov 30, 2018

Copyright © Suraiya Parveen, Ranjit Bisswas . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Suraiya Parveen, Ranjit Bisswas, “Applications of Semantic Similarity Metrics,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.413-416, 2018.

MLA Style Citation: Suraiya Parveen, Ranjit Bisswas "Applications of Semantic Similarity Metrics." International Journal of Computer Sciences and Engineering 6.11 (2018): 413-416.

APA Style Citation: Suraiya Parveen, Ranjit Bisswas, (2018). Applications of Semantic Similarity Metrics. International Journal of Computer Sciences and Engineering, 6(11), 413-416.

BibTex Style Citation:
@article{Parveen_2018,
author = {Suraiya Parveen, Ranjit Bisswas},
title = {Applications of Semantic Similarity Metrics},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2018},
volume = {6},
Issue = {11},
month = {11},
year = {2018},
issn = {2347-2693},
pages = {413-416},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3179},
doi = {https://doi.org/10.26438/ijcse/v6i11.413416}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i11.413416}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3179
TI - Applications of Semantic Similarity Metrics
T2 - International Journal of Computer Sciences and Engineering
AU - Suraiya Parveen, Ranjit Bisswas
PY - 2018
DA - 2018/11/30
PB - IJCSE, Indore, INDIA
SP - 413-416
IS - 11
VL - 6
SN - 2347-2693
ER -

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Abstract

The objective of this work is to access the applicability of the semantic similarity in concepts of a single ontology. The measurement of semantic similarity may help not only in information retrieval, but in other applications such as semantic search and semantic clustering. The traditional key-word search technique matches the keyword with the content of the document. These techniques do not reflect the meaning or relatedness. Hence the relevance and accuracy of the retrieved documents are less. Another important application of semantic similarity measurement is in cluster analysis. The semantic clusters may be treated with same function to accomplish the perfect analysis and decision making.

Key-Words / Index Term

Semantic Similarity, Ontology, Semantic Retrieval, Clustering

References

[1]. Tim B.L and Fischetti M., ―Weaving the Web: The Original Design andUltimate Destiny of the World Wide Web by its inventor‖, Britain: Orion Business. ISBN 0-7528-2090-7, 1999
[2]. Wang, J.Z., Ali, F.: An Efficient Ontology Comparison Tool for Semantic Web Applications. In: IEEE/WIC/ACM International Conference on Web Intelligence (WI 2005), pp. 372–378 (2005)
[3]. Hu, W., Jian, N., Qu, Y., Wang, Y.: GMO: A Graph Matching for Ontologies. In: Proceedings of Integrating Ontologies 2005, pp. 41-48. CEUR-WS.org, Aachen (2005)
[4]. Giunchiglia, F., Shvaiko, P., Yatskevich, M.: S-Match: an Algorithm and an Implementation of Semantic Matching. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 61–75. Springer, Heidelberg (2004)
[5]. Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics, 17–30 (1989)
[6]. Aggarwal N. (2012) Cross Lingual Semantic Search by Improving Semantic Similarity and Relatedness Measures. In: Cudré-Mauroux P. et al. (eds) The Semantic Web – ISWC 2012.. Lecture Notes in Computer Science, vol 7650. Springer, Berlin, Heidelberg
[7]. Moldovan, D.I., Mihalcea, R.: Using WordNet and lexical operators to improve Internet searches. IEEE Internet Computing 4(1), 34–43 (2000)
[8]. Finkelstein L., Gabrilovich E., Matias Y., Rivlin E., Solan Z., Wolfman G. and Ruppin E., ―Placing search in context: The concept revisited,ACM Transactions on Information Systems, Vol. 20, No.1, pp. 116–131, January 2002.
[9]. Schenkel, R., Theobald, A. & Weikum, G. Inf Retrieval (2005) 8: 521. https://doi.org/10.1007/s10791-005-0746-3
[10]. Liu, S., Liu, F., Yu, C., Meng, W.: An effective approach to document retrieval via utilizing WordNet and recognizing phrases. In: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, Sheffield, United Kingdom, pp. 266–272 (2004)
[11]. Sapkota, K., Thapa, L., Pandey, S.: Efficient Information Retrieval using measures of Semantic Similarity (2006)