Conceptual Web Users’ Actions Prediction for Ontology-Based Browsing Recommendations

  • Tarmo Robal
  • Ahto Kalja


The Internet consists of thousands of web sites with different kinds of structures. However, users are browsing the web according to their informational expectations towards the web site searched, having an implicit conceptual model of the domain in their minds. Nevertheless, people tend to repeat themselves and have partially shared conceptual views while surfing the web, finding some areas of web sites more interesting than others. Herein, we take advantage of the latter and provide a model and a study on predicting users’ actions based on the web ontology concepts and their relations.


Web usage mining Domain ontology modelling Web users conceptual profiling User behaviour prediction 


  1. 1.
    Bernard, M. L. (2001) User expectations for the location of web objects. In Proceedings of CHI ’01 Conference: Human Factors in Computing Systems, Seattle, WA, USA, March 31 – April 5, pp. 171–172.Google Scholar
  2. 2.
    Geissler, G., Zinkhan, G., and Watson, R. (2001) Web Home Page Complexity and Communication Effectiveness, Journal of the Association for Information Systems, 2(2): 1–48.Google Scholar
  3. 3.
    Bernard, M. L., and Chaparro, B. S. (2000) Searching within websites: A comparison of three types of sitemap menu structures. In Proceedings of The Human Factors and Ergonomics Society 44th Annual Meeting in San Diego, pp. 441–444. (available at
  4. 4.
    Lee, A.T. (1999) Web usability: a review of the research, ACM SIGCHI Bulletin, 31(1): 38–40.CrossRefGoogle Scholar
  5. 5.
    Srivastava, J. Cooley, R., Deshpande, M., and Tan, P-T. (2000) Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data, SIGKDD Explorations, 1(2): 12–23.CrossRefGoogle Scholar
  6. 6.
    Kolari, P., and Joshi, A. (2004) Web Mining – Research and Practice, IEEE Computing in Science and Engineering – Web Engineering Special Issue, 6(4): 49–53.Google Scholar
  7. 7.
    Berendt, B., Hotho, A., Mladenic, D., Someren, M., Spiliopoulou, M., and Stumme, G. (2004) A roadmap for web mining: From web to semantic web. In Berendt, B., Hotho, A., Mladenic, D., Someren, M., Spiliopoulou, M., and Stumme, G. (eds), First European Web Mining Forum, EWMF 2003, LNCS 3209, pp. 1–22, Springer, Heidelberg.Google Scholar
  8. 8.
    Eirinaki, M., Lampos, C., Paulakis, S., and Vazirgiannis, M. (2004) Web personalization integrating content semantics and navigational patterns. In WIDM '04: Proceedings of the 6th Annual ACM International Workshop on Web Information and Data Management, November 12–13, Washington DC, USA, pp. 72–79.Google Scholar
  9. 9.
    Baglioni, M., Ferrara, U., Romei, A., Ruggieri, S., and Turini, F. (2003) Preprocessing and mining web log data for web personalization. In Cappelli, A., and Turini, F. (eds), Proceedings of the 8th Congress of the Italian Association for Artificial Intelligence (AI*IA), Lecture Notes in Computer Science, 2829, Springer-Verlag, Berlin, 2003, pp. 237–249.Google Scholar
  10. 10.
    Lim, E-P., and Sun, A. (2005) Web mining – the ontology approach. In The International Advanced Digital Library Conference (IADLC’2005), August 25–26, Nagoya, Japan, (available at
  11. 11.
    Middleton, S., De Roure, D, and Shadbolt, N. (2001) Capturing knowledge of user preferences: ontologies in recommender systems. In Proceedings of the 1st International Conference on Knowledge Capture (K-CAP 2001), October 21–23, Victoria, BC, Canada, pp. 100–107.Google Scholar
  12. 12.
    Li, Y., and Zhong, N. (2006) Mining ontology for automatically acquiring web user information needs. IEEE Transactions on Knowledge and Data Engineering, 18(4): 554–568.MathSciNetCrossRefGoogle Scholar
  13. 13.
    Sarwar, B., Konstan, J., Borchers, A., Herlocker, J., Miller, B., and Reidl, J. (1998) Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In Proceedings of ACM Conference on Computer Supported Collaborative Work (CSCW), November 14–18, Seattle, Washington, USA, pp. 345–354.Google Scholar
  14. 14.
    Middleton, S. E., Shadbolt, N. R., and De Roure, D. C. (2003). Capturing interest through inference and visualization: ontological user profiling in recommender systems. In Proceedings of the 2nd International Conference on Knowledge Capture, October 23–25, Sanibel Island, FL, USA, pp. 62–69.Google Scholar
  15. 15.
    Shapira, B., Taieb-Maimon, M., and Moskowitz, A. (2006) Study of usefulness of known and new implicit indicators and their optimal combination for accurate inference of users interest. In Proc. of the 2006 ACM Symposium on Applied Computing (SAC '06), April 23–27, Dijon, France, pp. 1118–1119.Google Scholar
  16. 16.
    Davison, B. (1999) Web traffic logs: an imperfect resource for evaluation. In Proceedings of Ninth Annual Conference of the Internet Society (INET '99), June 22–25, San Jose, CA, (available at
  17. 17.
    Mobasher, B., Cooley, R., and Srivastava. J. (2000) Automatic personalization based on web usage mining. Communications of the ACM, 43(8): 142–151.CrossRefGoogle Scholar
  18. 18.
    Kimball, R., and Margy, R. (2002) The Data Warehouse Toolkit: The Complete Guide to Dimensional Modelling. John Wiley & Sons, New York, 2nd ed., 464p.Google Scholar
  19. 19.
    Robal, T., and Kalja, A. (2007) Applying user profile ontology for mining web site adaptation recommendations. In Ioannidis, Y., Novikov, B. and Rachev, B. (eds) 11th East-European Conference on Advances in Databases and Information Systems (ADBIS 2007), September 29 – October 03, Varna, Bulgaria., pp. 126–135.Google Scholar
  20. 20.
    Robal, T., Haav, H-M., and Kalja, A. (2007) Making web users' domain models explicit by applying ontologies. In Hainaut, J.-L., et al. (eds) Advances in Conceptual Modeling – Foundations and Applications: ER 2007 Workshops CMLSA, FP-UML,ONISW, QoIS, RIGiM, SeCoGIS, November 5–9, Auckland, New Zealand, Berlin: Springer, (LNCS), pp. 170–179.Google Scholar
  21. 21.
    Robal, T., and Kalja, A. (2008) A model for users' action prediction based on locality profiles. In Lang,M., Wojtkowski, W., Wojtkowski, G., Wrycza, S., and Zupancic, J. (eds). The Inter-Networked World: ISD Theory, Practice, and Education, Springer-Verlag, New York, ISBN 978-0387304038, to appear.Google Scholar
  22. 22.
    Robal, T., Kalja, A., and Põld, J. (2006) Analysing the web log to determine the efficiency of web systems. In Vasilecas, O., Eder, J., and Caplinskas, A. (eds) Proceedings of the 7th International Baltic Conference on Databases and Information Systems (DB&IS'2006), Communications, July 03–06, Vilnius, Lithuania, pp. 264–275.Google Scholar
  23. 23.
    The Protégé Ontology Editor and Knowledge Acquisition System, (available at

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Tarmo Robal
    • 1
  • Ahto Kalja
    • 1
  1. 1.Department of Computer EngineeringTallinn University of TechnologyTallinnEstonia

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