tain level of popularity optionally enriched by Wordnet's synonyms, for user's interest modeling for personalizing and adapting the web(and web 2.0 itself) In our future work, we plan to evaluate the impact of spreading activation on relationships between tags and compare two different spreading methods(via actors, via items). Our preliminary results showed that the spreading algorithm is able to create meaningful"jumps" in the hierarchy, but is highly dependent on number of links from and to tag(when there are too many links, the energy is divided into neglectable chunks). One way how to solve this issue is to introduce a variable starting energy, depending on a popularity of a given ta Acknowledgment. This work was partially supported by the Scientific Grant Agency of Slovak Republic, grant No. VG1/050809 and the Cultural and Edu- cational Grant Agency of the Slovak Republic, grant No. KEGA 3/ 5187/07 References 1. Heckmann, D, et al. GUMO- The General User Model Ontology. In Ardissonc L, et al., eds. User Modeling 2005. LNCS 3538, Springer(2005)428-432 2. Andrejko, A, Barla, M, Bielikova, M: Ontology-based User Modeling for Web- based Information Systems. In: Advances in Information Systems Development Springer(2007)457-468 3. Barla, M, TvaroZek, M., Bielikova, M. Rule-Based User Characteristics Acquisi tion from Logs With Semantics for Personalized Web-based Systems. Computing and Informatics(2009)accepted 4. Coyle, M, Smyth, B :(Web Search)shared: Social Aspects of a Collaborative Community-Based Search Network. In Nejdl, W., Kay, J, Pu, P, Herder, E, eds AH2008.LNCs5149, Springer(2008)103-112 5. Joachims, T, et al. Accurately interpreting clickthrough data as implicit feedback In: SIGIR2005,ACM(2005)154-161 6. Mika, P: Ontologies are us: A Unified Model of Social Networks and Semantics. J. Web Sen.5(1)(2007)5-15 7. Crestani, F. Application of Spreading Activation Techniques in Information Re- trieval. Artif. Intell. Rev. 11(6)(1997)453-482 Fellbaum, C. WordNet: An Electronical Lexical Database. The MIT Press, Cam- bridge, MA(1998 9. Schwarzkopf, E, Heckmann, D, Dengler, D, Kroner, A: Mining the Structure of ag Spaces for User Modeling. In: Data Mining for User Modeling, Workshop held atUM2007.(2007)6375 10. Schmitz. C. et al. Mining Association Rules in Folksonomies. Studies in Classi- fication, Data Analysis, and Knowledge Organization, Part VI. In: Data Science and Classification. Springer, Berlin Heidelberg(2006)261-270 11. Heymann, P, Garcia-Molina, H Collaborative Creation of Commu- nal Hierarchical Taxonomies in Social Tagging Systems. Technical re- port, Computer Science Department, Stanford University(2006)Available at http://heymann.stanford.edu/taghierarchy.html(29.06.2009) 12. Shepitsen, A, et al. Personalized Recommendation in Social Tagging Systems using Hierarchical Clustering. In: Recommender Systems 2008, New York, NY USA,ACM(2008)259266tain level of popularity optionally enriched by Wordnet’s synonyms, for user’s interest modeling for personalizing and adapting the web (and web 2.0 itself). In our future work, we plan to evaluate the impact of spreading activation on relationships between tags and compare two different spreading methods (via actors, via items). Our preliminary results showed that the spreading algorithm is able to create meaningful ”jumps” in the hierarchy, but is highly dependent on number of links from and to tag (when there are too many links, the energy is divided into neglectable chunks). One way how to solve this issue is to introduce a variable starting energy, depending on a popularity of a given tag. Acknowledgment. This work was partially supported by the Scientific Grant Agency of Slovak Republic, grant No. VG1/0508/09 and the Cultural and Edu￾cational Grant Agency of the Slovak Republic, grant No. KEGA 3/5187/07. References 1. Heckmann, D., et al.: GUMO – The General User Model Ontology. In Ardissono, L., et al., eds.: User Modeling 2005. LNCS 3538, Springer (2005) 428–432 2. Andrejko, A., Barla, M., Bielikov´a, M.: Ontology-based User Modeling for Web￾based Information Systems. In: Advances in Information Systems Development. Springer (2007) 457–468 3. Barla, M., Tvaroˇzek, M., Bielikov´a, M.: Rule-Based User Characteristics Acquisi￾tion from Logs With Semantics for Personalized Web-based Systems. Computing and Informatics (2009) accepted. 4. Coyle, M., Smyth, B.: (Web Search)shared: Social Aspects of a Collaborative, Community-Based Search Network. In Nejdl, W., Kay, J., Pu, P., Herder, E., eds.: AH 2008. LNCS 5149, Springer (2008) 103–112 5. Joachims, T., et al.: Accurately interpreting clickthrough data as implicit feedback. In: SIGIR 2005, ACM (2005) 154–161 6. Mika, P.: Ontologies are us: A Unified Model of Social Networks and Semantics. J. Web Sem. 5(1) (2007) 5–15 7. Crestani, F.: Application of Spreading Activation Techniques in Information Re￾trieval. Artif. Intell. Rev. 11(6) (1997) 453–482 8. Fellbaum, C.: WordNet: An Electronical Lexical Database. The MIT Press, Cam￾bridge, MA (1998) 9. Schwarzkopf, E., Heckmann, D., Dengler, D., Kr¨oner, A.: Mining the Structure of Tag Spaces for User Modeling. In: Data Mining for User Modeling, Workshop held at UM2007. (2007) 63–75 10. Schmitz, C., et al.: Mining Association Rules in Folksonomies. Studies in Classi- fication, Data Analysis, and Knowledge Organization, Part VI. In: Data Science and Classification. Springer, Berlin Heidelberg (2006) 261–270 11. Heymann, P., Garcia-Molina, H.: Collaborative Creation of Commu￾nal Hierarchical Taxonomies in Social Tagging Systems. Technical re￾port, Computer Science Department, Stanford University (2006) Available at: http://heymann.stanford.edu/taghierarchy.html (29.06.2009). 12. Shepitsen, A., et al.: Personalized Recommendation in Social Tagging Systems using Hierarchical Clustering. In: Recommender Systems 2008, New York, NY, USA, ACM (2008) 259–266