BibSonomy publications for /user/stumme/2007Mon Jun 09 12:37:50 CEST 2008Künstliche Intelligenz35-8Themenheft Web Mining20072007 myown introduction ki ir ml mining web Mon Jun 09 12:36:33 CEST 2008Künstliche Intelligenz35-8Mining the World Wide Web -- Methods, Ap- plications, and Perspectives2007myown 2007 mining introduction ki ml web ir Mon Jun 09 12:32:10 CEST 2008Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep50-54Conceptual Clustering of Social Bookmark Sites2007social folksonomy folksonomies tagorapub collaborative conceptual clustering myown bookmark bookmarking 2007 Social tagging Thu Jan 31 11:59:44 CET 2008Künstliche Intelligenz35-8Mining the World Wide Web --- Methods, Applications, and Perspectives2007myown web mining introduction 2007 ki Fri Jan 18 16:03:19 CET 2008AI Communications Journal, Special Issue on "Network Analysis in Natural Sciences and Engineering"4245-262Network Properties of Folksonomies202007fca tagorapub folksonomy folksonomies 2007 emergent semantics network l3s myown Fri Jan 18 13:47:10 CET 2008Graz, Austria7th International Conference on Knowledge Management (I-KNOW '07)SEP356-364Conceptual Clustering of Social Bookmarking Sites2007sites folksonomies social folksonomy 2007 tagorapub tagging myown 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.Wed Dec 12 19:23:10 CET 2007Deliverable 15.2, European Network of Excellence ``Prolearn - Professional E-Learning''Case study on social software use in distributed working environments2007bibsonomy software social study myown 2007 case Fri Nov 09 15:04:13 CET 2007Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep13-20Tag Recommendations in Folksonomies2007l3s filtering folksonomy collaborative recommender myown social bookmarking 2007 Wed Oct 31 14:01:46 CET 2007Berlin, HeidelbergProceedings of the 15th International Conference on Conceptual Structures (ICCS 2007)July283--295Lecture Notes in Artificial IntelligenceAnalysis of the Publication Sharing Behaviour in {BibSonomy}46042007folksonomy social sharing fca l3s bookmarking publication bibsonomy iccs myown trias BibSonomy 2007 BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.Mon Oct 22 20:09:38 CEST 2007BanffProc. WWW2007 Workshop ``Tagging and Metadata for Social Information Organization''MayNetwork Properties of Folksonomies2007smallworld myown sna folksonomy 2007 socialnetwork folksononomies l3s fca semantics emergent 8Tue Oct 16 16:49:55 CEST 2007Berlin, HeidelbergKnowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases506-514Lecture Notes in Computer ScienceTag Recommendations in Folksonomies47022007FolkRank recommendations myown folksonomies nepomuk l3s 2007 Folksonomies tagging ranking Recommendations 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.Thu Sep 20 13:38:06 CEST 2007Berlin, HeidelbergProceedings of the 15th International Conference on Conceptual Structures (ICCS 2007)conf/iccs/2006July241-254Lecture Notes in Artificial IntelligenceReducing the Representation Complexity of Lattice-Based Taxonomies46042007taxonomies representation formal iccs concept 2007 analysis fca complexity lattice Mon Jul 23 14:41:02 CEST 2007Banff, CanadaWorkshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007) at WWW 2007Organizing Publications and Bookmarks in BibSonomy2007organizing l3s tagorapub bibsonomy challenge myown 2007 BibSonomy Mon Apr 16 18:48:20 CEST 2007Banff, CanadaWorkshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007) at WWW 20072007l3s www2007 ckc2007 2007 myown challenge WWW collaborative social