Cattuto, C.; Schmitz, C.; Baldassarri, A.; Servedio, V. D. P.; Loreto, V.; Hotho, A.; Grahl, M. & Stumme, G.: Network Properties of Folksonomies. In:
AI Communications 20 (2007), Nr. 4, S. 245 - 262
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Grahl, M.; Hotho, A. & Stumme, G.: Conceptual Clustering of Social Bookmarking Sites.
7th International Conference on Knowledge Management (I-KNOW '07). Graz, Austria: Know-Center, 2007, S. 356-364
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Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.
Jäschke, R.; Marinho, L. B.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G.: Tag Recommendations in Folksonomies. In: Kok, J. N.; Koronacki, J.; de Mántaras, R. L.; Matwin, S.; Mladenic, D. & Skowron, A. (Hrsg.):
Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings. Springer, 2007 (Lecture Notes in Computer Science 4702), S. 506-514
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Begelman, G.; Keller, P. & Smadja, F.: Automated Tag Clustering: Improving search and exploration in the tag space. In:
WWW2006, May (2006), S. 22-26
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Heymann, P. & Garcia-Molina, H.:
Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems. , 2006
[Volltext] [Kurzfassung]
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Collaborative tagging systems---systems where many casual users annotate objects with free-form strings (tags) of their choosing---have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems.
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Information Retrieval in Folksonomies: Search and Ranking.
Proceedings of the 3rd European Semantic Web Conference . Budva, Montenegro: Springer, 2006 (LNCS 4011), S. 411-426
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Jäschke, R.; Hotho, A.; Schmitz, C.; Ganter, B. & Stumme, G.: TRIAS - An Algorithm for Mining Iceberg Tri-Lattices.
Proc. 6th ICDM conference. Hong Kong: 2006
[BibTeX]
Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G.: Mining Association Rules in Folksonomies. In: Batagelj, V.; Bock, H.-H.; Ferligoj, A. & Žiberna, A. (Hrsg.):
Data Science and Classification (Proc. IFCS 2006 Conference). Berlin/Heidelberg: Springer, 2006Studies in Classification, Data Analysis, and Knowledge Organization , S. 261-270
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Xu, Z.; Fu, Y.; Mao, J. & Su, D.: Towards the Semantic Web: Collaborative Tag Suggestions.
Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006. 2006
[BibTeX]
Golder, S. & Huberman, B. A.:
The Structure of Collaborative Tagging Systems. , 2005
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Mika, P.: Ontologies Are Us: A Unified Model of Social Networks and Semantics.
International Semantic Web Conference. Springer, 2005LNCS , S. 522-536
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