Ontology Partitioning: Clustering Based Approach

Автор: Soraya Setti Ahmed, Mimoun Malki, Sidi Mohamed Benslimane

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 6 Vol. 7, 2015 года.

Бесплатный доступ

The semantic web goal is to share and integrate data across different domains and organizations. The knowledge representations of semantic data are made possible by ontology. As the usage of semantic web increases, construction of the semantic web ontologies is also increased. Moreover, due to the monolithic nature of the ontology various semantic web operations like query answering, data sharing, data matching, data reuse and data integration become more complicated as the size of ontology increases. Partitioning the ontology is the key solution to handle this scalability issue. In this work, we propose a revision and an enhancement of K-means clustering algorithm based on a new semantic similarity measure for partitioning given ontology into high quality modules. The results show that our approach produces meaningful clusters than the traditional algorithm of K-means.

Еще

Ontology, Partition Algorithm, Modularization, Ontology Owl, K-Means Clustering Algorithm, Similarity Measures

Короткий адрес: https://sciup.org/15012306

IDR: 15012306

Список литературы Ontology Partitioning: Clustering Based Approach

  • Z. Marouf, S.B. Benslimane, “An integrated Approach to drive ontological structure from folksonomie”. International journal of information technology and computer science. Vol (6), pp.35-45, 5, December, 2014
  • G. Grau, B. Parsia, E.Sirin and A.Kalyanpur, “Modularizing owl ontologies”. In Proceedings of the KCAP-2005 Workshop on Ontology Management, Ban, Canada.
  • P. Doran, V. Tamma., L.ao. Iannone, J. Caragea, V. Honavar, “Ontology module extraction for ontology reuse “. In: the CIKM, ACM. 61-70. 2007
  • W. Ceusters, B. Smith, L. Goldberg, “A Terminological and Ontological Analysis of the NCI Thesaurus” preprint version of paper in Methods of Information in Medicine, 44, 498-507. 2005
  • C. Grau, B. Horrocks, I., Kazakov, Y., Sattler, “Representation and Reasoning” (CRR 2006), collocated with ECAI 2006 (2006) Modular reuse of ontology Theory and practice. J. of Artificial Intelligence Research (JAIR) 273-318.2006.
  • P. Ignazio, A.Valentina, M., Tamma, R.Terry, P., Paul Doran., “Task Oriented Evaluation of Module Extraction Techniques”. International Semantic Web Conference. pp.130-145
  • J. Philbin, O. Chum, M.Isard, J.,Sivic, A.Zisserman, “Object retrieval with large vocabularies and fast spatial matching”. In: CVPR 2007.
  • A. Rector, A. Napoli, G. Stamou, G. Stoilos, H. Wache, Je_ Pan, M. d'Aquin, S. Spaccapietra, and V. Tzouvaras, “Report on modularization of ontologies”. Technical report, Knowledge Web Deliverable D2.1.3.1, 2005.
  • A. Dennai, S.M. Benslimane, “Toward an Update of a Similarity Measurement for a Better Calculation of the Semantic Distance between Ontology Concepts”. The Second International Conference on Informatics Engineering & Information Science (ICIEIS2013). Kuala Lumpur, Malaysia, November 12-14, 2013.
  • Z. Wu and M. Palmer. “Verb semantics and lexical selection”. In Proceedings of the 32nd Annual Meeting of the Associations for Computational Linguistics, pp 133-138. 1994.
  • S. Sellami, A. Benharkat, Y. Amghar R Rifaieh, “Study of Challenges and Techniques in Large Scale Matching”. In Proceedings of the 10th International Conference on Enterprise Information Systems, Barcelona, Spain. pp. 355-361. 2008.
  • X. Zhang Cheng G, Y. Qu., “Ontology summarization based on rdf sentence graph”. In Proceedings of the 16th International Conference on World Wide Web, New York, NY, USA. ACM press. pp. 707-716. 2007.
  • H. Stuckenschmidt and M.C.A. Klein, “Structure-Based Partitioning of Large Concept Hierarchies”, In 3rd International Semantic Web Conference, pages 289–303. LNCS 3298, Springer-Verlag, 2004.
  • B. Cuenca-Grau, B. Parsia, E. Sirin, and A. Kalyanpur. “Automatic Partitioning of OWL Ontologies Using E-Connections”. In Proceedings of the 2005 International Workshop on Description Logics (DL-2005), 2005.
  • O. Kutz, C. Lutz, F. Wolter, and M. Zakharyaschev, “E- connections of abstract description systems”. Artificiel Intelligence, 156(1):1-73, 2004.
  • A. Borgida and L. Serafini, “Distributed description logics: Directed domain correspondences in federated information sources”. In R. Meersman, Z. Tari, and et al, editors, On the Move to Meaningful Internet Systems 2002: CoopIS, DOA, and ODBASE: Confederated International Conferences CoopIS, DOA, and ODBASE 2002. Proceedings, volume 2519 of Lecture Notes in Computer Science, pages 36-53. Springer Berlin, 2002.
  • K. Saruladha, G. Aghila, B. Sathiya, “A Partitioning Algorithm for Large Scale Ontologies”. International Conference on Recent Trends In Information Technology (ICRTIT), 2012
  • R. Kolli. “Scalable Matching Of Ontology Graphs Using Partitioning”. M.S.Thesis, University of Georgia. Kunjir MP, 2008.
  • W.Hu, Y. Zhao, Y.Qu, “Partition-based block matching of large class hierarchies”. In Asian Semantic Web Conference, pp 72-83. 2006.
  • S. Guha, R.Rastogi, and Shim, K. ROCK, “A Robust Clustering Algorithm for Categorical Attributes”. In Proceedings of the 15th International Conference on Data Engineering (Sydney, Australia. March 23-26 1999.
  • A. Schlicht, H. Stuckenschmidt, “Criteria-Based Partitioning of Large Ontologies”. In Proceedings of the 4th international conference on Knowledge capture (KCAP), ACM press. pp. 171-172. 2007
  • X. Huang, W.Lai, “Clustering graphs for visualization via node similarities”. J. Vis. Lang. Comput. 17(3):225-253.
  • B. Cuenca Grau, B. Parsia, E Sirin, A. Kalyanpur, “Automatic Partitioning of OWL Ontologies Using E-Connections”, International Workshop on Description Logics. 2005.
  • M.P. Kunjir, MD. Pujari, Project Report on Effective and Efficient computation of Cluster Similarity. M.S. Thesis, Indian Institute of Science, Bangalore 2009.
  • A. Ghanbarpour and H. Abolhassani, “Partitioning large ontologies based on their structures”. International Journal of Physical Sciences Vol. 7(40), pp. 5545-5551, 23 October, 2012
  • C. Sang, G. Suh Lavanya, “Role of Clustering of Ontology Relations for Preventive Health Care through Nutrition”. Technical report, unpublished.
  • G. Ding, T. Sun, Y. Xu, “Multi-Schema Matching Based On Clustering Techniques”. In the 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). 2013.
  • F. Esposito, N. Fanizzi, and C. d’Amato, “Conceptual Clustering Applied to Ontologies by means of Semantic Discernability”, unpublished.
  • Z. Jiang, S. Qingguo T. Tang, Li Y ongiang, “An aggregation cache replacement algorithm based on ontology clustering”. Journal of natural sciences. Vol. 11 NO.5 1141-1146.2006
  • S. Karol, V. Mangat. “Evaluation of a Text Document Clustering Approach based on Particle Swarm Optimization”. IJCSNS International Journal of Computer Science and Network Security, Vol.13 No.7, July 2013.
  • R. B-Yates and B. Ribeiro-Neto, “Modern Information Retrieval”. ACM Press, Addison-Wesley: New York, Harlow, England Reading, Mass., 1999.
  • G. Salton. and M. J.McGill, “Introduction to modern information retrieval”. McGraw-Hill. New York, 1983.
  • R. Rada, H. Mili, E. Bichnelland M. Blettner, “Development and application of a metric on semantic nets”, IEEE Transaction on Systems, Man, and Cybernetics: pp 17-30. 1989.
  • J.H. Lee, M.H. Kimand, Y.J. Lee, “Information Retrieval Based on Conceptual Distance in IS-A Hierarchy”, Journal of Documentation 49, pp. 188-207, 1993.
  • G. Salton and M. J.McGill, “Introduction to modern information retrieval”. McGraw-Hill. New York, 1983.
  • T. Slimani, B. Ben Yaghlane, and K. Mellouli, “A New Similarity Measure based on Edge Counting”. In World Academy of Science, Engineering and Technology 23 2008.
Еще
Статья