@article{jaeschke08discovering, author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd}, title = {Discovering Shared Conceptualizations in Folksonomies}, journal = {Journal of Web Semantics}, year = {2008}, volume = {6}, number = {1}, pages = {38-53}, url = {http://dx.doi.org/10.1016/j.websem.2007.11.004}, keywords = {2008, analysis, bibsonomy, concept, discovering, fca, folksonomies, formal, itegpub, l3s, myown, shared, triadic} }

%0 = article %A = Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd %D = 2008 %T = Discovering Shared Conceptualizations in Folksonomies %U = http://dx.doi.org/10.1016/j.websem.2007.11.004

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.

@inproceedings{jaeschke2008logsonomy, author = {Jäschke, Robert and Krause, Beate and Hotho, Andreas and Stumme, Gerd}, title = {Logsonomy -- A Search Engine Folksonomy}, booktitle = {Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)}, publisher = {AAAI Press}, year = {2008}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf}, keywords = {2008, engine, folksonomies, folksonomy, itegpub, logsonomies, logsonomy, myown, search, tagorapub}, abstract = {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.} }

%0 = inproceedings %A = Jäschke, Robert and Krause, Beate and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008) %D = 2008 %I = AAAI Press %T = Logsonomy -- A Search Engine Folksonomy %U = http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf

@inproceedings{krause2008antisocialb, author = {Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd}, title = {The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems}, booktitle = {Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web}, year = {2008}, url = {http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf}, keywords = {2.0, 2008, bookmarking, folksonomies, folksonomy, itegpub, myown, social, spam, systems, tagger, tagorapub, web, web2.0} }

%0 = inproceedings %A = Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd %B = Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web %D = 2008 %T = The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems %U = http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf

@article{cattuto2007networkb, author = {Cattuto, Ciro and Schmitz, Christoph and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio and Hotho, Andreas and Grahl, Miranda and Stumme, Gerd}, title = {Network Properties of Folksonomies}, editor = {Hoche, Susanne and Nürnberger, Andreas and Flach, Jürgen}, journal = {AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering''}, publisher = {IOS Press}, year = {2007}, volume = {20}, number = {4}, pages = {245-262}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf}, issn = {0921-7126}, keywords = {2007, emergent, fca, folksonomies, folksonomy, itegpub, l3s, myown, network, semantics, tagorapub} }

%0 = article %A = Cattuto, Ciro and Schmitz, Christoph and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio and Hotho, Andreas and Grahl, Miranda and Stumme, Gerd %D = 2007 %I = IOS Press %T = Network Properties of Folksonomies %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf

@inproceedings{grahl07conceptualKdml, author = {Grahl, Miranda and Hotho, Andreas and Stumme, Gerd}, title = {Conceptual Clustering of Social Bookmark Sites}, editor = {Hinneburg, Alexander}, booktitle = {Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)}, publisher = {Martin-Luther-Universität Halle-Wittenberg}, year = {2007}, pages = {50-54}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf}, isbn = {978-3-86010-907-6}, keywords = {2007, Social, bookmark, bookmarking, clustering, collaborative, conceptual, folksonomies, folksonomy, itegpub, myown, social, tagging, tagorapub} }

%0 = inproceedings %A = Grahl, Miranda and Hotho, Andreas and Stumme, Gerd %B = Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007) %D = 2007 %I = Martin-Luther-Universität Halle-Wittenberg %T = Conceptual Clustering of Social Bookmark Sites %U = http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf

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.

@inproceedings{grahl2007conceptuala, author = {Grahl, Miranda and Hotho, Andreas and Stumme, Gerd}, title = {Conceptual Clustering of Social Bookmarking Sites}, booktitle = {7th International Conference on Knowledge Management (I-KNOW '07)}, publisher = {Know-Center}, address = {Graz, Austria}, year = {2007}, pages = {356-364}, url = {http://www.tagora-project.eu/wp-content/2007/06/grahl_iknow07.pdf}, keywords = {2007, folksonomies, folksonomy, itegpub, myown, sites, social, tagging, tagorapub}, abstract = {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.} }

%0 = inproceedings %A = Grahl, Miranda and Hotho, Andreas and Stumme, Gerd %B = 7th International Conference on Knowledge Management (I-KNOW '07) %C = Graz, Austria %D = 2007 %I = Know-Center %T = Conceptual Clustering of Social Bookmarking Sites %U = http://www.tagora-project.eu/wp-content/2007/06/grahl_iknow07.pdf

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.
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.

@inproceedings{jaeschke2007tag, author = {Jäschke, Robert and Marinho, Leandro Balby and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, title = {Tag Recommendations in Folksonomies}, editor = {Kok, Joost N. and Koronacki, Jacek and de Mántaras, Ramon López and Matwin, Stan and Mladenic, Dunja and Skowron, Andrzej}, booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin, Heidelberg}, year = {2007}, volume = {4702}, pages = {506-514}, url = {http://dx.doi.org/10.1007/978-3-540-74976-9_52}, isbn = {978-3-540-74975-2}, keywords = {2007, FolkRank, Folksonomies, Recommendations, folksonomies, itegpub, l3s, myown, nepomuk, ranking, recommendations, tagging}, abstract = {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.
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.} }

%0 = inproceedings %A = Jäschke, Robert and Marinho, Leandro Balby and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd %B = Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases %C = Berlin, Heidelberg %D = 2007 %I = Springer %T = Tag Recommendations in Folksonomies %U = http://dx.doi.org/10.1007/978-3-540-74976-9_52

@inproceedings{jaeschke06trias, author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd}, title = {TRIAS - An Algorithm for Mining Iceberg Tri-Lattices}, booktitle = {Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06)}, publisher = {IEEE Computer Society}, address = {Hong Kong}, year = {2006}, pages = {907-911}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/jaeschke2006trias.pdf}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.162}, isbn = {0-7695-2701-9}, keywords = {2006, FCA, OntologyHandbook, algorithm, analysis, concept, fca, folksonomies, folksonomy, formal, iceberg, itegpub, lattices, myown, nepomuk, tagging, tri, triadic, trias} }

%0 = inproceedings %A = Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd %B = Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06) %C = Hong Kong %D = 2006 %I = IEEE Computer Society %T = TRIAS - An Algorithm for Mining Iceberg Tri-Lattices %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2006/jaeschke2006trias.pdf