Benz, D.; Tso, K. H. L. & Schmidt-Thieme, L.: Supporting Collaborative Hierarchical Classification: Bookmarks as an Example. In:
Special Issue of the Computer Networks journal on Innovations in Web Communications Infrastructure (2007),
[BibTeX]
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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Cattuto, C.; Loreto, V. & Servedio, V. D.: A Yule-Simon process with memory. In:
Europhysics Letters 76 (2006), Nr. 2, S. 208-214
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The Yule-Simon model has been used as a tool to describe the growth of diverse systems, acquiring a paradigmatic character in many fields of research. Here we study a modified Yule-Simon model that takes into account the full history of the system by means of a hyperbolic memory kernel. We show how the memory kernel changes the properties of preferential attachment and provide an approximate analytical solution for the frequency distribution density as well as for the frequency-rank distribution.
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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Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Trend Detection in Folksonomies. In: Avrithis, Y. S.; Kompatsiaris, Y.; Staab, S. & O'Connor, N. E. (Hrsg.):
Proc. First International Conference on Semantics And Digital Media Technology (SAMT). Springer, 2006 (Lecture Notes in Computer Science 4306), S. 56-70
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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]
Marlow, C.; Naaman, M.; Boyd, D. & Davis, M.: Position Paper, Tagging, Taxonomy, Flickr, Article, ToRead.
Collaborative Web Tagging Workshop at WWW2006. 2006
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In recent years, tagging systems have become increasingly popular. These systems enable users to add keywords (i.e., “tags”) to Internet resources (e.g., web pages, images, videos) without relying on a controlled vocabulary. Tagging systems have the potential to improve search, spam detection, reputation systems, and personal organization while introducing new modalities of social communication and opportunities for data mining. This potential is largely due to the social structure that underlies many of the current systems. Despite the rapid expansion of applications that support tagging of resources, tagging systems are still not well studied or understood. In this paper, we provide a short description of the academic related work to date. We offer a model of tagging systems, specifically in the context of web-based systems, to help us illustrate the possible benefits of these tools. Since many such systems already exist, we provide a taxonomy of tagging systems to help inform their analysis and design, and thus enable researchers to frame and compare evidence for the sustainability of such systems. We also provide a simple taxonomy of incentives and contribution models to inform potential evaluative frameworks. While this work does not present comprehensive empirical results, we present a preliminary study of the photosharing and tagging system Flickr to demonstrate our model and explore some of the issues in one sample system. This analysis helps us outline and motivate possible future directions of research in tagging systems.
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
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Golder, S. & Huberman, B. A.:
The Structure of Collaborative Tagging Systems. , 2005
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Hammond, T.; Hannay, T.; Lund, B. & Scott, J.: Social Bookmarking Tools (I). In:
D-Lib Magazine (2005),
[BibTeX]
Lund, B.; Hammond, T.; Flack, M. & Hannay, T.: Social Bookmarking Tools (II): A Case Study - Connotea. In:
D-Lib Magazine 11 (2005), Nr. 4,
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Michail, A.:
CollaborativeRank: Motivating People to Give Helpful and Timely Ranking Suggestions. , 2005
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Szekely, B. & Torres, E.:
Ranking Bookmarks and Bistros: Intelligent Community and Folksonomy Development. , 2005
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Newman, M.: Small Worlds: The Structure of Social Networks. In:
Complexity (2000),
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Kleinberg, J. M.: Authoritative sources in a hyperlinked environment. In:
Journal of the ACM 46 (1999), Nr. 5, S. 604-632
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. The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics,...
Page, L.; Brin, S.; Motwani, R. & Winograd, T.: The PageRank citation ranking: Bringing order to the Web.
Proceedings of the 7th International World Wide Web Conference. Brisbane, Australia: 1998, S. 161-172
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