CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Comparative Analysis of Ontology and Schema Matching Systems

by K. Saruladha, Dr. G. Aghila, B. Sathiya
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 34 - Number 8
Year of Publication: 2011
Authors: K. Saruladha, Dr. G. Aghila, B. Sathiya
10.5120/4118-5981

K. Saruladha, Dr. G. Aghila, B. Sathiya . A Comparative Analysis of Ontology and Schema Matching Systems. International Journal of Computer Applications. 34, 8 ( November 2011), 14-21. DOI=10.5120/4118-5981

@article{ 10.5120/4118-5981,
author = { K. Saruladha, Dr. G. Aghila, B. Sathiya },
title = { A Comparative Analysis of Ontology and Schema Matching Systems },
journal = { International Journal of Computer Applications },
issue_date = { November 2011 },
volume = { 34 },
number = { 8 },
month = { November },
year = { 2011 },
issn = { 0975-8887 },
pages = { 14-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume34/number8/4118-5981/ },
doi = { 10.5120/4118-5981 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:20:33.715964+05:30
%A K. Saruladha
%A Dr. G. Aghila
%A B. Sathiya
%T A Comparative Analysis of Ontology and Schema Matching Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 34
%N 8
%P 14-21
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In a distributed and open system, such as the semantic web and many other applications like information integration, peer- peer communication, etc., the heterogeneity among the data increases enormously. To solve the heterogeneity issue various matching techniques are proposed and large-scale matching needs especially to be supported for different kinds of ontologies and XML schemas due to their increasing use and size, e.g., in life science applications, e-business and web. In this paper the techniques which are scalable like early pruning, partitioning, parallelization and some renowned scalable matching techniques are discussed. In addition to it, a brief comparison of the discussed matching techniques is also presented.

References
  1. Shvaiko P, Euzenat J, “Ten challenges for ontology matching,” Confederated International Conference on the Move to Meaningful Internet Systems, pp. 1164–1182,2008.
  2. Rahm E, “Towards Large-Scale Schema and Ontology Matching,” Schema matching and mapping, Bellahsene Z, Bonifati A Rahm E, eds. New York: Springer Heidelberg, pp. 3–27, 2011.
  3. Smith K, Morse M, Mork P et al., “The role of schema matching in large enterprises,” Proceedings of CIDR, 2009.
  4. Su W, Wang J, Lochovsky FH, “Holistic schema matching for web query interfaces,” Proceedings of international conference on extending database technology (EDBT). Springer, Heidelberg, pp 77–94, 2006.
  5. Kirsten T, Thor A, Rahm E, “Instance-based matching of large life science ontologies,” Proceedings of data integration in the life sciences (DILS). LNCS, Springer, Heidelberg, vol. 4544, pp 172–187, 2007.
  6. Zhang S, Mork P, Bodenreider O, Bernstein PA, “Comparing two approaches for aligning representations of anatomy,” Artif Intell Med vol. 39, no. 3, pp 227–236, 2007.
  7. Avesani P, Giunchiglia F, Yatskevich M, “A large scale taxonomy mapping evaluation,” Proceedings of international conference on semantic web (ICSW). LNCS, vol. 3729. Springer, Heidelberg, pp 67–81, 2005.
  8. P. Shvaiko, J. Euzenat, “ A survey of schema-based matching approaches,” Journal on Data Semantics IV, LNCS, Springer, vol. 3730, pp. 146-171, 2005.
  9. Peukert E, Berthold H, Rahm E, “Rewrite techniques for performance optimization of schema matching processes,” Proceedings of 13th international conference on extending database technology (EDBT). ACM, NY, pp 453–464, 2010.
  10. Ehrig M, Staab S, “Quick ontology matching,” Proceedings of international conference semantic web (ICSW). LNCS, vol 3298. Springer, Heidelberg, pp 683–697, 2004.
  11. Hanif MS, Aono M, “An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size,” Journal of Web Semantics, vol. 7, no. 4, pp. 344–356, 2009.
  12. Do HH, Rahm E, “Matching large schemas: Approaches and evaluation,” Journal of Information System, vol. 3, no. 6, pp. 857–885, 2007.
  13. Hu W, Qu Y, Cheng G, “Matching large ontologies: A divide-and-conquer-approach,” Journal of Data Knowledge Engineering, vol. 67, no. 1, pp. 140–160, 2008.
  14. Hamdi F, Safar B, Reynaud C, Zargayouna H, “Alignment-based partitioning of large-scale ontologies,” Advances in knowledge discovery and management, 1st ed. New York: Springer Heidelberg, pp. 251–269, 2009.
  15. Y. Qu, W. Hu, G. Cheng, “Constructing virtual documents for ontology matching,” Proceedings of the 15th International World Wide Web Conference, ACM Press, pp. 23–31, 2006.
  16. Stoilos G, Stamou G, Kollias S, “A string metric for ontology alignment,” Proceedings of the 4th International Semantic Web Conference, LNCS, Springer, vol. 3729, pp. 624–637, 2005.
  17. W. Hu, N. Jian, Y. Qu, Y. Wang, “GMO: a graph matching for ontologies,” Proceedings of the K-CAP Workshop on Integrating Ontologies, pp. 41–48, 2005.
  18. Guha S, Rastogi R, Shim K, “ROCK: a robust clustering algorithm for categorical attributes,” Proceedings of the 15th International Conference on Data Engineering, pp. 512–521, 1999.
  19. Yuzhong Qu, Wei Hu, and Gong Chen, “Constructing virtual documents for ontology matching,” Proc. 15th International World Wide Web Conference (WWW), Edinburgh (UK), pp 23–31, 2006.
  20. Gross A, Hartung M, Kirsten T, Rahm E, “On matching large life science ontologies in parallel,” Proceedings of 7th international conference on data integration in the life sciences (DILS). LNCS, vol 6254. Springer, Heidelberg, 2010.
  21. Cruz IF, Antonelli FP, Stroe C, “AgreementMaker: Efficient matching for large real-world schemas and ontologies,” PVLDB, VLDB Endowment, vol. 2, no. 2, pp 1586–1589, 2009.
  22. Jean-Mary YR, Shironoshita EP, Kabuka MR, “Ontology matching with semantic verification,” J Web Sem vol. 7, no. 3, pp. 235–251, 2009.
  23. Li J, Tang J, Li Y, Luo Q, “RiMOM:A dynamic multistrategy ontology alignment framework,” IEEE Trans Knowl Data Eng vol. 21 , no. 8, pp. 1218–1232, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Similarity Measure Schema Matching Ontology Matching Ontology Alignment Image Segmentation K means

Powered by PhDFocusTM