Advertisement

Investigating Similarity of Ontology Instances and Its Causes

  • Anton Andrejko
  • Mária Bieliková
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5164)

Abstract

In this paper we present a novel method of comparing instances of ontological concepts in regard to personalized presentation and/or navigation in large information spaces. It is based on the assumption that comparing attributes of documents which were found interesting for a user can be a source for discovering information about user’s interests. We consider applications for the Semantic Web where documents or their parts are represented by ontological concepts. We employ ontology structure and different similarity metrics for data type and object type attributes. From personalization point of view we impute reasons that might have caused user’s interest in the content. Moreover, we propose a way to enumerate similarity for the particular user while taking into account individual user’s interests and preferences.

Keywords

User Characteristic Ontological Concept Ontology Mapping Compute Similarity Taxonomy Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brusilovsky, P., Tasso, C.: Preface to special issue on user modeling for web information retrieval. UMUAI 14(2-3), 147–157 (2004)Google Scholar
  2. 2.
    Návrat, P., Bieliková, M., Rozinajová, V.: Acquiring, organising and presenting information and knowledge from the web. In: Comp. Sys. Tech. 2006 (2006)Google Scholar
  3. 3.
    Tury, M., Bieliková, M.: An approach to detection ontology changes. In: ICWE 2006: Workshop Proc. of 6th Int. Conf. on Web Eng. ACM Press, Palo Alto (2006)Google Scholar
  4. 4.
    Rodríguez, M.A., Egenhofer, M.J.: Determining semantic similarity among entity classes from different ontologies. IEEE Transactions on Knowledge and Data Engineering 15(2), 442–456 (2003)CrossRefGoogle Scholar
  5. 5.
    Liu, X., Wang, Y., Wang, J.: Towards a semi-automatic ontology mapping. In: Proc. of 5th Mexican Int. Conf. on Artificial Intelligence (MICAI 2006). IEEE, Los Alamitos (2006)Google Scholar
  6. 6.
    Doan, A., Madhavan, J., Domingos, P., Halevy, A.: Learning to map between ontologies on the semantic web. In: WWW 2002: Proceedings of the 11th international conference on World Wide Web, pp. 662–673. ACM, New York (2002)CrossRefGoogle Scholar
  7. 7.
    Formica, A., Missikoff, M.: Concept similarity in symontos: An enterprise management tool. The Computer Journal 45(6), 583–595 (2002)zbMATHCrossRefGoogle Scholar
  8. 8.
    Pázman, R.: Ontology search with user preferences. In: Tools for Acquisition, Organisation and Presenting of Information and Knowledge, pp. 139–147 (2006)Google Scholar
  9. 9.
    Bernstein, A., Kaufmann, E., Burki, C., Klein, M.: How similar is it? towards personalized similarity measures in ontologies. In: 7th International Conference Wirtschaftsinformatik (WI-2005), Bamberg, Germany, pp. 1347–1366 (2005)Google Scholar
  10. 10.
    Ding, L., Kolari, P., Ding, Z., Avancha, S.: Using ontologies in the semantic web: A survey. In: Sharman, R., et al. (ed.) Ontologies: A Handbook of Principles, Concepts and Applications in Information Systems, pp. 79–113. Springer, Heidelberg (2007)Google Scholar
  11. 11.
    Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11, 95–130 (1999)zbMATHGoogle Scholar
  12. 12.
    Andrejko, A., Barla, M., Bieliková, M.: Ontology-based user modeling for web-based inf. systems. In: Advances in Information Systems Development New Methods and Practice for the Networked Society, vol. 2, pp. 457–468. Springer, Heidelberg (2007)Google Scholar
  13. 13.
    Barla, M., Bieliková, M.: Estimation of user characteristics using rule-based analysis of user logs. In: Data Mining for User Modeling Proceedings of Workshop held at the International Conference on User Modeling UM 2007, pp. 5–14 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Anton Andrejko
    • 1
  • Mária Bieliková
    • 1
  1. 1.Institute of Informatics and Software Engineering Faculty of Informatics and Information technologiesSlovak University of TechnologyBratislava 

Personalised recommendations