Vol. 10 No. 1 (2011): Revista UIS Ingenierías
Articles

Model of a semantic web meta-browser based on a general knowledge taxonomy, a general domain ontology, specific ontologies and user profile

Hugo Ordoñez-Eraso
Universidad del Cauca
Bio
Carlos Alberto Cobos-Lozada
Universidad del Cauca
Bio
Elizabeth León-Guzmán
Universidad Nacional de Colombia sede Bogotá
Bio

Published 2011-06-15

Keywords

  • Meta-web searcher,
  • taxonomy,
  • ontology,
  • WordNet,
  • user profile

How to Cite

Ordoñez-Eraso, H., Cobos-Lozada, C. A., & León-Guzmán, E. (2011). Model of a semantic web meta-browser based on a general knowledge taxonomy, a general domain ontology, specific ontologies and user profile. Revista UIS Ingenierías, 10(1), 23–28. Retrieved from https://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/23-38

Abstract

Web search has become one of the most important fields of research around the world. They are many reasonsincluding: the fast-growing nature of information sources; the search necessity for information closer to specificuser requirements; the need to reduce search time; and the desire to take into account the semantics of terms usedwhen doing search queries. This paper shows a semantic meta-web search model called XGhobi which uses indexedresources by Google, Yahoo! and Bing. The XGhobi engine combines a general taxonomy of knowledge, a generaldomain ontology –WordNet-, a set of specific domain ontologies, and user profile management to improve therelevance of recovered documents in both English and Spanish. A detailed description of the meta-web searchengine’s components, some user interfaces and its results and its assessments are shown. The assessment covers theobtained precision on tests done by users.

Downloads

Download data is not yet available.

References

  1. Baeza-Yates, R., A. and B. Ribeiro-Neto, Modern Information Retrieval. 1999: Addison-Wesley Longman Publishing Co., Inc. 513.
  2. Manning, C., P. Raghavan, and H. Schütze, An Introduction to Information Retrieval. 2007, Cambridge University Press: Cambridge, England.
  3. Liaw, S.-S. and H.-M. Huang, Information retrieval from the World Wide Web: a user-focused approach based on individual experience with search engines. Computers in Human Behavior, 2006. 22(3): p. 501-517.
  4. Massimo, M., A basis for information retrieval in context. ACM Trans. Inf. Syst., 2008. 26(3): p. 1-41.
  5. Manning, C., P. Raghavan, and H. Schütze, Introduction to Information Retrieval. 2008, Cambridge University Press: Cambridge, England.
  6. Eui-Hong, H., et al., Intelligent metasearch engine for knowledge management, in Proceedings of the twelfth international conference on Information and knowledge management %@ 1-58113-723-0. 2003, ACM: New Orleans, LA, USA. p. 492-495.
  7. Mustafa, J., S. Khan, and K. Latif. Ontology based semantic information retrieval. in Intelligent Systems, 2008. IS ‘08. 4th International IEEE Conference. 2008.
  8. Susan, G., S. Mirco, and P. Alexander, OntologyBased User Profiles for Search and Browsing, in Ontologies, S. US, Editor. 2007. p. 665-694.
  9. Karatzoglou, A. and I. Feinerer. Text Clustering with String Kernels in {R}. in Advances in Data Analysis (Proceedings of the 30th Annual Conference of the Gesellschaft f{ü}r Klassifikation e.V., Freie Universit{ä}t Berlin, March 8--10, 2006). 2007: Springer-Verlag.
  10. Etsioni, E.S.a.O. Multi-service search and comparison using the MetaCrawler. in 4th International World Wide Web Conference. 1995.
  11. Dogpile.com. Different Engines, Different Results: Web Searchers Not Always Finding What They’re Looking for Online. 2007; Available from: http://www.infospaceinc.com/onlineprod/ Overlap-DifferentEnginesDifferentResults.pdf.
  12. Carpineto, C., et al., A survey of Web clustering engines. ACM Comput. Surv., 2009. 41(3): p. 1-38.
  13. Barry, C.L., User-Defined Relevance Criteria: An Exploratory Study. Journal of the American Society for Information Science-A, 1994. 45(1): p. 149-159.
  14. Huang, A., et al. Clustering Documents with Active Learning Using Wikipedia. in Data Mining, 2008. ICDM ‘08. Eighth IEEE International Conference on. 2008.
  15. Li, X. Research on Text Clustering Algorithm Based on K_means and SOM. in Intelligent Information Technology Application Workshops, 2008. IITAW ‘08. International Symposium on. 2008.
  16. Mao-Ting, G. and W. Zheng-Ou. A New Algorithm for Text Clustering Based on Projection Pursuit. in Machine Learning and Cybernetics, 2007 International Conference on. 2007.
  17. Fuzhi, Z., et al. An Ant-Based Fast Text Clustering Approach Using Pheromone. in Fuzzy Systems and Knowledge Discovery, 2008. FSKD ‘08. Fifth International Conference on. 2008.
  18. Guo, Q.-l. and M. Zhang, Semantic information integration and question answering based on pervasive agent ontology. Expert Systems with Applications, 2009. 36: p. 10.
  19. Anil, K.J., Data Clustering: 50 Years Beyond K-means, in Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I. 2008, SpringerVerlag: Antwerp, Belgium.
  20. Jing, L., Survey of Text Clustering. 2008.
  21. Song, J.-f., et al., Ontology-Based Information Retrieval Model for the Semantic Web, in Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE’05) on e-Technology, e-Commerce and e-Service. 2005, IEEE Computer Society.
  22. Aufaure, M.A., R. Soussi, and H. Baazaoui. SIRO: On-line semantic information retrieval using ontologies. in Digital Information Management, 2007. ICDIM ‘07. 2nd International Conference on. 2007.
  23. Giannis, V., et al., Semantic similarity methods in wordNet and their application to information retrieval on the web, in Proceedings of the 7th annual ACM international workshop on Web information and data management. 2005, ACM: Bremen, Germany.
  24. Beck, H.W., T. Anwar, and S.B. Navathe, A conceptual clustering algorithm for database schema design. Knowledge and Data Engineering, IEEE Transactions on, 1994. 6(3): p. 396-411.
  25. Song, W., C.H. Li, and S.C. Park, Genetic algorithm for text clustering using ontology and evaluating the validity of various semantic similarity measures. Expert Systems with Applications, 2009. 36(5): p. 9095-9104.
  26. Bhatia, S.K. and J.S. Deogun, Conceptual clustering in information retrieval. Systems, Man, and Cybernetics, Part B, IEEE Transactions on, 1998. 28(3): p. 427-436.
  27. Liu, H. and H. Motoda, Computational Methods of Feature Selection. 2007: Chapman & Hall/ CRC.
  28. Salton, G. and C. Buckley, Improving retrieval performance by relevance feedback. Journal of the American Society for Information, 1999. 41(4): p. 288 - 297.
  29. Rich, E., User modeling via stereotypes. 1979: p. 329-354.
  30. Ordoñez, H. and C. Cobos. Ghobi – Un Meta Buscador Web Optimizado Para Búsquedas En Español. in Quinto Congreso Colombiano de Computación. 2010. Cartagena, Colombia.
  31. Salton, G. and C. Buckley, Term-weighting approaches in automatic text retrieval. Information Processing & Management, 1988. 24(5): p. 513-523.
  32. Song, W. and S.C. Park, Genetic algorithm for text clustering based on latent semantic indexing. Computers & Mathematics with Applications, 2009. 57(11-12): p. 1901-1907.
  33. Giugni O., M. and R. Loaiza B., Metodología para el desarrollo de portales centrada en el usuario: una evaluación empírica. Revista electrónica de estudios telemáticos, 2008. 7(3): p. 17.
  34. Fisher, D.H., Knowledge acquisition via incremental conceptual clustering. Machine Learning, 1987. 2(2): p. 139-172.
  35. Montero, Y.H., Factores del Diseño Web Orientado a la Satisfacción y No-Frustración de Uso. Revista Española de Documentación Científica, 2006: p. 239-257.
  36. Martínez, F., Propuesta y desarrollo de un modelo para la evaluación de la recuperación de información en Internet, in Información y Documentación. 2002, Universidad de Murcia: Murcia, España. p. 283.
  37. Cacheda, F., V. Formoso, and V. Carneiro, Performance Analysis of Distributed Web Information Retrieval Systems. Latin America Transactions, IEEE (Revista IEEE America Latina), 2007. 5(6): p. 479-485.
  38. Can, F., R. Nuray, and A.B. Sevdik, Automatic performance evaluation of Web search engines. Information Processing & Management, 2004. 40(3): p. 495-514.
  39. Chen, S., D. Alahakoon, and M. Indrawan. Building an Adaptive Hierarchy of Clusters for Text Data. in Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on. 2005.
  40. Zhao, L., et al. An improved measuring similarity for short text snippets and its application in clustering search engine. in Machine Learning and Cybernetics, 2008 International Conference on. 2008.
  41. Forsati, R., et al. Hybridization of K-Means and Harmony Search Methods for Web Page Clustering. in Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT ‘08. IEEE/WIC/ ACM International Conference on. 2008.
  42. Garcia, E. RSJ-PM Tutorial: A Tutorial on the Robertson-Sparck Jones Probabilistic Model for Information Retrieval. 2009; Available from: http://www.miislita.com/information-retrievaltutorial/information-retrieval-probabilisticmodel-tutorial.pdf.