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Centralized Clustering Method To Increase Accuracy In Ontology Matching Systems | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 1، دوره 45، شماره 2، بهمن 2013، صفحه 1-10 اصل مقاله (803.99 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2015.524 | ||
| نویسندگان | ||
| Samira Babalou1؛ Mohammad Javad Kargar* 2؛ Seyyed Hashem Davarpanah2 | ||
| 1MSC student, Department of Computer Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran | ||
| 2Assistant Professor, Department of Computer Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran | ||
| چکیده | ||
| Ontology is the main infrastructure of the Semantic Web which provides facilities for integration, searching and sharing of information on the web. Development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. By emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory consumption. Therefore, partitioning the ontology was proposed. In this paper, a new clustering method for the concepts within ontologies is proposed, which is called SeeCC. The proposed method is a seeding-based clustering method which reduces the complexity of comparison by using clusters’ seed. The SeeCC method facilitates the memory consuming problem and increases their accuracy in the large-scale matching problem as well. According to the evaluation of SeeCC's results with Falcon-AO and the proposed system by Algergawy accuracy of the ontology matching is easily observed. Furthermore, compared to OAEI (Ontology Alignment Evaluation Initiative), SeeCC has acceptable result with the top ten systems. | ||
| کلیدواژهها | ||
| Ontology matching؛ Clustering method؛ Large-scale matching؛ Semantic graph | ||
| عنوان مقاله [English] | ||
| Centralized Clustering Method To Increase Accuracy In Ontology Matching Systems | ||
| نویسندگان [English] | ||
| Samira Babalou1؛ Mohammad Javad Kargar2؛ Seyyed Hashem Davarpanah2 | ||
| 1MSC student, Department of Computer Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran | ||
| 2Assistant Professor, Department of Computer Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran | ||
| چکیده [English] | ||
| Ontology is the main infrastructure of the Semantic Web which provides facilities for integration, searching and sharing of information on the web. Development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. By emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory consumption. Therefore, partitioning the ontology was proposed. In this paper, a new clustering method for the concepts within ontologies is proposed, which is called SeeCC. The proposed method is a seeding-based clustering method which reduces the complexity of comparison by using clusters’ seed. The SeeCC method facilitates the memory consuming problem and increases their accuracy in the large-scale matching problem as well. According to the evaluation of SeeCC's results with Falcon-AO and the proposed system by Algergawy accuracy of the ontology matching is easily observed. Furthermore, compared to OAEI (Ontology Alignment Evaluation Initiative), SeeCC has acceptable result with the top ten systems. | ||
| کلیدواژهها [English] | ||
| Ontology matching, Clustering method, Large-scale matching, Semantic graph | ||
| مراجع | ||
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[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. | ||
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