Centralized Clustering Method To Increase Accuracy In Ontology Matching Systems

[1] Hendler, J. “Agents and the semantic web,” Intelligent Systems, IEEE, vol. 16, no.2, pp. 30-37, 2001.
[2] Euzenat, J., C. Meilicke, H. Stuckenschmidt, P. Shvaiko, and C. Trojahn, “Ontology alignment evaluation initiative: six years of experience,” in Journal on data semantics XV, Springer. pp. 158-192, 2011.
[3] Euzenat, J., A. Ferrara, W. van Hage, L. Hollink, C. Meilicke, A. Nikolov, et al. “Results of the

Ontology Alignment Evaluation Initiative 2011.” in 6th OM workshop, 2011.
[4] Hu, W, Y. Qu, and G. Cheng, “Matching large ontologies: A divide-and-conquer approach,” Data & Knowledge Engineering, vol. 67, no. 1, pp. 140-160, 2008.
[5] Algergawy, A., S. Massmann, and E. Rahm. “A clustering-based approach for large-scale ontology matching,” in Advances in Databases and Information Systems, Springer, 2011.
[6] Wang, Z., Y. Wang, S. Zhang, G. Shen, and T. Du, “Matching large scale ontology effectively,” in The Semantic Web–ASWC 2006, Springer, pp. 99-105, 2006.
[7] Khan, M., N. Javaid, M. Khan, A. Javaid, Z. Khan, and U. Qasim, “Hybrid DEEC: Towards Efficient Energy Utilization in Wireless Sensor Networks,” arXiv preprint arXiv:1303.4679, 2013.
[8] Bsoul, M., A. Al-Khasawneh, A.E. Abdallah, E.E. Abdallah, and I. Obeidat, “An energy-efficient threshold-based clustering protocol for wireless sensor networks,” Wireless personal communications, vol. 70, no. 1, pp. 99-112, 2013.
[9] Saruladha, K., G. Aghila, and B. Sathiya. “A partitioning algorithm for large scale ontologies,” in Recent Trends In Information Technology (ICRTIT), 2012 International Conference on. IEEE, 2012.
[10] Zhou, Y, H. Cheng, and J. X. Yu, “Graph clustering based on structural/attribute similarities,” Proceedings of the VLDB Endowment, vol. 2, no. 1, pp. 718-729, 2009.
[11] Hu, W. and Y. Qu, “Falcon-AO: A practical ontology matching system. Web Semantics,” Science, Services and Agents on the World Wide Web, vol. 6, no.3, pp. 237-239, 2008.
[12] Do, H.-H. and E. Rahm, “Matching large schemas Approaches and evaluation,” Information Systems, vol. 32, no.6, pp. 857-885, 2007.
[13] Hu, W., Y. Zhao, and Y. Qu, “Partition-based block matching of large class hierarchies,” in The Semantic Web–ASWC 2006, Springer, pp. 72-83, 2006.
[14] Jiménez-Ruiz, E. and B.C. Grau, “Logmap: Logic-based and scalable ontology matching,” in The Semantic Web–ISWC 2011, Springer, pp. 273-288, 2011.
[15] Kirsten, T., A. Gross, M. Hartung, and E. Rahm, “GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution,” J. Biomedical Semantics, vol. 2, pp. 6, 2011.
[16] Ngo, D. and Z. Bellahsene, “YAM++: a multi-strategy based approach for ontology matching task,” in Knowledge Engineering and Knowledge Management, Springer, pp. 421-425, 2012.
[17] Grau, B.C., I. Horrocks, Y. Kazakov, and U. Sattler. “Just the right amount: extracting modules from ontologies,” in Proceedings of the 16th international conference on World Wide Web, ACM, 2007.
[18] Wang, Z., Y. Wang, S. Zhang, G. Shen, and T. Du, “Ontology Pasing Graph-based Mapping: A Parsing Graph-based Algorithm for Ontology Mapping,” Journal of Donghua University, vol. 23, no.6, pp. 5, 2006.
[19] Yuruk, N., M. Mete, X. Xu, and T.A. Schweiger. “AHSCAN: Agglomerative hierarchical structural clustering algorithm for networks. in Social Network Analysis and Mining,” ASONAM'09. International Conference on Advances in. 2009 IEEE, 2009.
[20] Guha, S., R. Rastogi, and K. Shim, “ROCK: A robust clustering algorithm for categorical attributes,” Information systems, vol. 25, no.5, pp. 345-366, 2000.
[21] Hamdi, F., B. Safar, C. Reynaud, and H. Zargayouna, “Alignment-based partitioning of large-scale ontologies,” in Advances in knowledge discovery and management, Springer, pp. 251-269, 2010.
[22] Zhang, X., H. Li, and Y. Qu," Finding important vocabulary within ontology ", in The Semantic Web–ASWC 20062006, Springer. p. 106-112..
[23] Graves, A., S. Adali, and J. Hendler. “A Method to Rank Nodes in an RDF Graph,” International Semantic Web Conference (Posters & Demos). 2008.
[24] Kermarrec, A.-M., E. Le Merrer, B. Sericola, and G. Trédan, “Second order centrality: Distributed assessment of nodes criticity in complex networks,” Computer Communications, vol. 34, no. 5, pp. 619-628, 2011.
[25] Freeman, L.C," A set of measures of centrality based on betweenness ". Sociometry, 1977: p. 35-41.
[26] Hage, P. and F. Harary, “Eccentricity and centrality in networks,” Social networks, vol. 17, no.1, pp. 57-63, 1995.
[27] Koschützki, D., K.A. Lehmann, L. Peeters, S. Richter, D. Tenfelde-Podehl, and O. Zlotowski, “Centrality indices,” Network analysis, Springer, pp. 16-61, 2005.
[28] Zhang, X., G. Cheng, and Y. Qu, “Ontology summarization based on rdf sentence graph,”Proceedings of the 16th international conference on World Wide Web, ACM, 2007.
[29] Stuckenschmidt, H, “Network analysis as a basis for partitioning class hierarchies,” W8: Semantic Network Analysis, pp. 43, 2005.
[30] Algergawy, A., R. Nayak, and G. Saake, “Element similarity measures in XML schema matching,” Information Sciences, vol. 180, no. 24, pp. 4975-4998, 2010.
[31] Levenshtein, V.I., “Binary codes capable of correcting deletions, insertions and reversals,” in Soviet physics doklady, 1966.
[32] Lin, F. and K. Sandkuhl, “A survey of exploiting wordnet in ontology matching,” in Artificial Intelligence in Theory and Practice II2008, Springer, pp. 341-350, 2008.