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Using Knowledge Integration Techniques for User Profile Adaptation Method in Document Retrieval Systems

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Transactions on Computational Collective Intelligence V

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 6910))

Abstract

Knowledge integration is a very important and useful technique to combine information from different sources and different formats. In the Information Retrieval field, integration of knowledge can be understood in many ways. In this paper a method of user personalization in information retrieval using integration technique is presented. As user delivers new knowledge about himself, this knowledge should be integrated with previous knowledge contained in the user profile. Our proposed method is analyzed in terms of integration postulates. A list of desirable properties of this method and its proofs are presented. Simulated experimental evaluation has shown that proposed method is effective and the updated profile is proper since it is closer and closer to the user preferences.

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References

  1. Aamodt, A., Nygard, M.: Different roles and mutual dependencies of data, information, and knowledge - An AI perspective on their integration. Data & Knowledge Engineering 16, 191–222 (1995)

    Article  Google Scholar 

  2. Abecker, A., Decker, S.: Organizational Memory: Knowledge Acquisition, Integration, and Retrieval Issues. In: Puppe, F. (ed.) XPS 1999. LNCS (LNAI), vol. 1570, pp. 113–124. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  3. Bae, J.K., Kim, J.: Integration of heterogeneous models to predict consumer behavior. Expert Systems with Applications 37, 1821–1826 (2010)

    Article  Google Scholar 

  4. Barthelemy, J.P.: The Median Procedure for n-Trees. Journal of Classification 3, 329–334 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  5. Brewka, G., Eiter, T.: From Data Integration towards Knowledge Mediation. In: Erdem, E., Lin, F., Schaub, T. (eds.) LPNMR 2009. LNCS, vol. 5753, pp. 610–612. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  6. Chen, Y.J.: Knowledge integration and sharing for collaborative molding product design and process development. Computers in Industry 61, 659–675 (2010)

    Article  Google Scholar 

  7. Cohen, W.W.: Integration of Heterogeneous Databases Without Common Domains Using Queries Based on Textual Similarity. ACM SIGMOD Record Homepage 27(2) (1998)

    Google Scholar 

  8. Conesa, J., Storey, V.C., Sugumaran, V.: Improving web-query processing through semantic knowledge. Data & Knowledge Engineering 66, 18–34 (2008)

    Article  Google Scholar 

  9. Daniłowicz, C.: Models of Information Retrieval Systems with Special Regard to Users’ Preferences. Scientific Papers of the Main Library and Scientific Information Center of the Wrocław University of Technology, No.6 Monographs, No.3 Wrocław (1992)

    Google Scholar 

  10. Ding, H.: Towards the Metadata Integration Issues in Peer-to-Peer Based Digital Libraries. In: Jin, H., Pan, Y., Xiao, N., Sun, J. (eds.) GCC 2004. LNCS, vol. 3251, pp. 851–854. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  11. Dingming, W., Dongyan, Z., Xue, Z.: An Adaptive User Profile Based on Memory Model. In: The Ninth International Conference on Web-Age Information Management. IEEE, Los Alamitos (2008)

    Google Scholar 

  12. Jlaiel, N., Ben Ahmed, M.: Ontology and Agent Based Model for Software Development Best Practices’ Integration in a Knowledge Management System. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2006 Workshops. LNCS, vol. 4278, pp. 1028–1037. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  13. Lee, M.L., Yang, L.H., Hsu, W., Yang, X.: XClust: Clustering XML Schemas for Effective Integration. In: CIKM 2002, McLean, Virginia, USA, November 4-9 (2002)

    Google Scholar 

  14. Main Library and Scientific Information Centre in Wroclaw University of Technology, http://www.bg.pwr.wroc.pl/

  15. Merugu, S., Ghosh, J.: A Distributed Learning Framework for Heterogeneous Data Sources. In: KDD 2005, Chicago, Illinois, USA, August 21-24 (2005)

    Google Scholar 

  16. Maleszka, M., Mianowska, B., Nguyen, N.T.: Agent Technology for Information Retrieval in Internet. In: Håkansson, A., Nguyen, N.T., Hartung, R.L., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2009. LNCS, vol. 5559, pp. 151–162. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  17. Mianowska, B., Nguyen, N.T.: A Framework of an Agent-Based Personal Assistant for Internet Users. In: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J., Jain, L.C. (eds.) KES-AMSTA 2010. LNCS, vol. 6070, pp. 163–172. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  18. Mianowska, B., Nguyen, N.T.: A Method of User Modeling and Relevance Simulation in Document Retrieval Systems. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2011. LNCS (LNAI), vol. 6682, pp. 138–147. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  19. Mianowska, B., Nguyen, N.T.: A Method for User Profile Adaptation in Document Retrieval. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part II. LNCS (LNAI), vol. 6592, pp. 181–192. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  20. Mitchell, T.: Machine Learning. McGraw Hill, Singapore (1997)

    MATH  Google Scholar 

  21. Myaeng, S.H., Korfhage, R.R.: Integration of User Profiles: Models and Experiments in Information Retrieval. Information Processing & Management 26, 719–738 (1990)

    Article  Google Scholar 

  22. Nguyen, N.T.: Advanced Information and Knowledge Processing. Springer, London (2008)

    Google Scholar 

  23. Nemati, H.R., Steiger, D.M., Iyer, M.S., Herschel, R.T.: Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decision Support Systems 33, 143–161 (2002)

    Article  Google Scholar 

  24. Riaño, D., Moreno, A., Isern, D., Bocio, J., Sánchez, D., Jiménez, L.: Knowledge Exploitation from the Web. In: Karagiannis, D., Reimer, U. (eds.) PAKM 2004. LNCS (LNAI), vol. 3336, pp. 175–185. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  25. Rocchio, J.J.: Relevance Feedback in Information Retrieval. In: Salton, G. (ed.) The SMART Retrieval System: Experiments in Automatic Document Processing, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)

    Google Scholar 

  26. Suzuki, Y., Hatano, K., Yoshikawa, M., Uemura, S., Kawagoe, K.: A Relevant Score Normalization Method Using Shannon’s Information Measure. In: Fox, E.A., Neuhold, E.J., Premsmit, P., Wuwongse, V. (eds.) ICADL 2005. LNCS, vol. 3815, pp. 311–316. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  27. WordNet, A lexical database for English, http://wordnet.princeton.edu/

  28. Yearwood, J., Stranieri, A.: The integration of retrieval, reasoning and drafting for refugee law: a third generation legal knowledge based system. In: ICAIL 1999 Proceedings of the 7th International Conference on Artificial Intelligence and Law (1999)

    Google Scholar 

  29. Zhang, D., Lee, W.S.: Web Taxonomy Integration using Support Vector Machines. In: WWW 2004, New York, USA, May 17-22 (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Bernadetta Mianowska & Ngoc Thanh Nguyen

Authors
  1. Bernadetta Mianowska
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  2. Ngoc Thanh Nguyen
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Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

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Mianowska, B., Nguyen, N.T. (2011). Using Knowledge Integration Techniques for User Profile Adaptation Method in Document Retrieval Systems. In: Nguyen, N.T. (eds) Transactions on Computational Collective Intelligence V. Lecture Notes in Computer Science, vol 6910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24016-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-24016-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24015-7

  • Online ISBN: 978-3-642-24016-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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