Abstract
Many keyword-based approaches to text classification, information retrieval or even user modeling for adaptive web-based system could benefit from knowledge on relations between various keywords, which gives further possibilities to compare them, evaluate their distance etc. This paper proposes an approach how to determine keyword relations (mainly a parent-child relationship) by leveraging collective wisdom of the masses, present in data of collaborative (social) tagging systems on the Web. The feasibility of our approach is demonstrated on the data coming from the social bookmarking systems delicious and CiteULike.
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References
Heckmann, D., et al.: GUMO – The General User Model Ontology. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 428–432. Springer, Heidelberg (2005)
Andrejko, A., Barla, M., Bieliková, M.: Ontology-based User Modeling for Web-based Information Systems. In: Advances in Information Systems Development, pp. 457–468. Springer, Heidelberg (2007)
Barla, M., Tvarožek, M., Bieliková, M.: Rule-Based User Characteristics Acquisition from Logs With Semantics for Personalized Web-based Systems. Computing and Informatics (2009) (accepted)
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, vol. 5149, pp. 103–112. Springer, Heidelberg (2008)
Joachims, T., et al.: Accurately interpreting clickthrough data as implicit feedback. In: SIGIR 2005, pp. 154–161. ACM, New York (2005)
Mika, P.: Ontologies are us: A Unified Model of Social Networks and Semantics. J. Web Sem. 5(1), 5–15 (2007)
Crestani, F.: Application of Spreading Activation Techniques in Information Retrieval. Artif. Intell. Rev. 11(6), 453–482 (1997)
Fellbaum, C.: WordNet: An Electronical Lexical Database. The MIT Press, Cambridge (1998)
Schwarzkopf, E., Heckmann, D., Dengler, D., Kröner, A.: Mining the Structure of Tag Spaces for User Modeling. In: Data Mining for User Modeling, Workshop held at UM 2007, pp. 63–75 (2007)
Schmitz, C., et al.: Mining Association Rules in Folksonomies. In: Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization, Part VI, pp. 261–270. Springer, Heidelberg (2006)
Heymann, P., Garcia-Molina, H.: Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems. Technical report, Computer Science Department, Stanford University (2006), http://heymann.stanford.edu/taghierarchy.html (June 29, 2006)
Shepitsen, A., et al.: Personalized Recommendation in Social Tagging Systems using Hierarchical Clustering. In: Recommender Systems 2008, pp. 259–266. ACM, New York (2008)
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Barla, M., Bieliková, M. (2009). On Deriving Tagsonomies: Keyword Relations Coming from Crowd. In: Nguyen, N.T., Kowalczyk, R., Chen, SM. (eds) Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems. ICCCI 2009. Lecture Notes in Computer Science(), vol 5796. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04441-0_27
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DOI: https://doi.org/10.1007/978-3-642-04441-0_27
Publisher Name: Springer, Berlin, Heidelberg
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