TY - JOUR AU - Jäschke, Robert AU - Hotho, Andreas AU - Schmitz, Christoph AU - Ganter, Bernhard AU - Stumme, Gerd T1 - Discovering Shared Conceptualizations in Folksonomies JO - Journal of Web Semantics PY - 2008/ VL - 6 IS - 1 SP - 38 EP - 53 UR - http://dx.doi.org/10.1016/j.websem.2007.11.004 DO - KW - 2008 KW - analysis KW - bibsonomy KW - concept KW - discovering KW - fca KW - folksonomies KW - formal KW - itegpub KW - l3s KW - myown KW - shared KW - triadic L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Jäschke, Robert AU - Krause, Beate AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Logsonomy -- A Search Engine Folksonomy T2 - Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008) PB - AAAI Press C1 - PY - 2008/ CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf DO - KW - 2008 KW - engine KW - folksonomies KW - folksonomy KW - itegpub KW - logsonomies KW - logsonomy KW - myown KW - search KW - tagorapub L1 - SN - N1 - N1 - AB - In social bookmarking systems users describe bookmarksby keywords called tags. The structure behindthese social systems, called folksonomies, can beviewed as a tripartite hypergraph of user, tag and resourcenodes. This underlying network shows specificstructural properties that explain its growth and the possibilityof serendipitous exploration.Search engines filter the vast information of the web.Queries describe a user’s information need. In responseto the displayed results of the search engine, users clickon the links of the result page as they expect the answerto be of relevance. The clickdata can be represented as afolksonomy in which queries are descriptions of clickedURLs. This poster analyzes the topological characteristicsof the resulting tripartite hypergraph of queries,users and bookmarks of two query logs and compares ittwo a snapshot of the folksonomy del.icio.us. ER - TY - CONF AU - Krause, Beate AU - Schmitz, Christoph AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems T2 - Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web PB - C1 - PY - 2008/ CY - VL - IS - SP - EP - UR - http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf DO - KW - 2.0 KW - 2008 KW - bookmarking KW - folksonomies KW - folksonomy KW - itegpub KW - myown KW - social KW - spam KW - systems KW - tagger KW - tagorapub KW - web KW - web2.0 L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Cattuto, Ciro AU - Schmitz, Christoph AU - Baldassarri, Andrea AU - Servedio, Vito D. P. AU - Loreto, Vittorio AU - Hotho, Andreas AU - Grahl, Miranda AU - Stumme, Gerd T1 - Network Properties of Folksonomies JO - AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering'' PY - 2007/ VL - 20 IS - 4 SP - 245 EP - 262 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf DO - KW - 2007 KW - emergent KW - fca KW - folksonomies KW - folksonomy KW - itegpub KW - l3s KW - myown KW - network KW - semantics KW - tagorapub L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Grahl, Miranda AU - Hotho, Andreas AU - Stumme, Gerd A2 - Hinneburg, Alexander T1 - Conceptual Clustering of Social Bookmark Sites T2 - Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007) PB - Martin-Luther-Universität Halle-Wittenberg C1 - PY - 2007/10 CY - VL - IS - SP - 50 EP - 54 UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf DO - KW - 2007 KW - Social KW - bookmark KW - bookmarking KW - clustering KW - collaborative KW - conceptual KW - folksonomies KW - folksonomy KW - itegpub KW - myown KW - social KW - tagging KW - tagorapub L1 - SN - 978-3-86010-907-6 N1 - N1 - AB - ER - TY - CONF AU - Grahl, Miranda AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Conceptual Clustering of Social Bookmarking Sites T2 - 7th International Conference on Knowledge Management (I-KNOW '07) PB - Know-Center C1 - Graz, Austria PY - 2007/10 CY - VL - IS - SP - 356 EP - 364 UR - http://www.tagora-project.eu/wp-content/2007/06/grahl_iknow07.pdf DO - KW - 2007 KW - folksonomies KW - folksonomy KW - itegpub KW - myown KW - sites KW - social KW - tagging KW - tagorapub L1 - SN - N1 - N1 - AB - Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of thesystem, 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. ER - TY - CONF AU - Jäschke, Robert AU - Marinho, Leandro Balby AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd A2 - Kok, Joost N. A2 - Koronacki, Jacek A2 - de Mántaras, Ramon López A2 - Matwin, Stan A2 - Mladenic, Dunja A2 - Skowron, Andrzej T1 - Tag Recommendations in Folksonomies T2 - Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases PB - Springer C1 - Berlin, Heidelberg PY - 2007/ CY - VL - 4702 IS - SP - 506 EP - 514 UR - http://dx.doi.org/10.1007/978-3-540-74976-9_52 DO - KW - 2007 KW - FolkRank KW - Folksonomies KW - Recommendations KW - folksonomies KW - itegpub KW - l3s KW - myown KW - nepomuk KW - ranking KW - recommendations KW - tagging L1 - SN - 978-3-540-74975-2 N1 - N1 - AB - Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.

In this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably. ER - TY - CONF AU - Jäschke, Robert AU - Hotho, Andreas AU - Schmitz, Christoph AU - Ganter, Bernhard AU - Stumme, Gerd A2 - T1 - TRIAS - An Algorithm for Mining Iceberg Tri-Lattices T2 - Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06) PB - IEEE Computer Society C1 - Hong Kong PY - 2006/12 CY - VL - IS - SP - 907 EP - 911 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/jaeschke2006trias.pdf DO - http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.162 KW - 2006 KW - FCA KW - OntologyHandbook KW - algorithm KW - analysis KW - concept KW - fca KW - folksonomies KW - folksonomy KW - formal KW - iceberg KW - itegpub KW - lattices KW - myown KW - nepomuk KW - tagging KW - tri KW - triadic KW - trias L1 - SN - 0-7695-2701-9 N1 - N1 - AB - ER -