TY - BOOK AU - Balby Marinho, L. AU - Hotho, A. AU - Jäschke, R. AU - Nanopoulos, A. AU - Rendle, S. AU - Schmidt-Thieme, L. AU - Stumme, G. AU - Symeonidis, P. A2 - T1 - Recommender Systems for Social Tagging Systems PB - Springer C1 - PY - 2012/02 VL - IS - SP - EP - UR - http://link.springer.com/book/10.1007/978-1-4614-1894-8 DO - 10.1007/978-1-4614-1894-8 KW - recommender KW - social_tagging KW - baarbeit KW - toread L1 - SN - 978-1-4614-1893-1 N1 - N1 - AB - Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models. ER - TY - BOOK AU - Balby Marinho, L. AU - Hotho, A. AU - Jäschke, R. AU - Nanopoulos, A. AU - Rendle, S. AU - Schmidt-Thieme, L. AU - Stumme, G. AU - Symeonidis, P. A2 - T1 - Recommender Systems for Social Tagging Systems PB - Springer C1 - PY - 2012/02 VL - IS - SP - EP - UR - http://link.springer.com/book/10.1007/978-1-4614-1894-8 DO - 10.1007/978-1-4614-1894-8 KW - tagging KW - itegpub KW - recommender KW - collaborative KW - social KW - l3s KW - folksonomy KW - bookmarking KW - myown KW - 2012 KW - info20 KW - tagging KW - 2012 L1 - SN - 978-1-4614-1893-1 N1 - N1 - AB - Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models. ER - TY - BOOK AU - Balby Marinho, L. AU - Hotho, A. AU - Jäschke, R. AU - Nanopoulos, A. AU - Rendle, S. AU - Schmidt-Thieme, L. AU - Stumme, G. AU - Symeonidis, P. A2 - T1 - Recommender Systems for Social Tagging Systems PB - Springer C1 - PY - 2012/02 VL - IS - SP - EP - UR - http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1 DO - KW - tagging KW - recommender KW - collaborative KW - social KW - folksonomy KW - bookmarking KW - myown KW - 2012 L1 - SN - 978-1-4614-1893-1 N1 - N1 - AB - Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models. ER - TY - BOOK AU - Balby Marinho, L. AU - Hotho, A. AU - Jäschke, R. AU - Nanopoulos, A. AU - Rendle, S. AU - Schmidt-Thieme, L. AU - Stumme, G. AU - Symeonidis, P. A2 - T1 - Recommender Systems for Social Tagging Systems PB - Springer C1 - PY - 2012/02 VL - IS - SP - EP - UR - http://link.springer.com/book/10.1007/978-1-4614-1894-8 DO - 10.1007/978-1-4614-1894-8 KW - tagging KW - recommender KW - collaborative KW - social KW - folksonomy KW - bookmarking KW - myown KW - 2012 L1 - SN - 978-1-4614-1893-1 N1 - N1 - AB - Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models. ER - TY - JOUR AU - Mitzlaff, Folke AU - Stumme, G T1 - Relatedness of given names JO - Human Journal PY - 2012/ VL - 1 IS - 4 SP - 205 EP - 217 UR - DO - KW - relatedness KW - similarity KW - nameling KW - cosine L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Benz, D AU - Hotho, A AU - Jaschke, R AU - Krause, B AU - Mitzlaff, F AU - Schmitz, C AU - Stumme, G T1 - The social bookmark and publication management system bibsonomy A platform for evaluating and demonstrating Web 2.0 research JO - VLDB JOURNAL PY - 2010/12 VL - 19 IS - 6 SP - EP - UR - DO - KW - bibsonomy L1 - SN - N1 - N1 - AB - Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research. ER - TY - JOUR AU - Benz, D. AU - Hotho, A. AU - Jaschke, R. AU - Stumme, G. AU - Halle, A. AU - Lima, A. G. S. AU - Steenweg, H. AU - Stefani, S. AU - Lalmas, M. AU - Jose, J. AU - Rauber, A. AU - Sebastiani, F. AU - Frommholz, I. T1 - Academic Publication Management with PUMA - Collect, Organize and Share Publications JO - LECTURE NOTES IN COMPUTER SCIENCE PY - 2010/1 VL - IS - 6273 SP - EP - UR - DO - KW - PUMA L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Benz, D. AU - Hotho, A. AU - Jaschke, R. AU - Stumme, G. AU - Halle, A. AU - Lima, A. G. S. AU - Steenweg, H. AU - Stefani, S. AU - Lalmas, M. AU - Jose, J. AU - Rauber, A. AU - Sebastiani, F. AU - Frommholz, I. T1 - Academic Publication Management with PUMA - Collect, Organize and Share Publications JO - LECTURE NOTES IN COMPUTER SCIENCE PY - 2010/1 VL - IS - 6273 SP - EP - UR - DO - KW - PUMA L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Cattuto, C. AU - Schmitz, C. AU - Baldassarri, A. AU - Servedio, V. D. P. AU - Loreto, V. AU - Hotho, A. AU - Grahl, M. AU - Stumme, G. T1 - Network Properties of Folksonomies JO - AI Communications PY - 2007/ VL - 20 IS - 4 SP - 245 EP - 262 UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2007/aicomm_2007_folksonomy_clustering.pdf DO - KW - clustering KW - folksonomy KW - community L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Gonzalez-Olalla, J. AU - Stumme, G. A2 - Berendt, B. A2 - Hotho, A. A2 - Stumme, G. T1 - Semantic Methods and Tools for Information Portals - The SemIPort Project (Project Description) T2 - Semantic Web Mining. Proc. of the Semantic Web Mining Workshop of the 13th Europ. Conf. PB - C1 - Helsinki PY - 2002/august 19, CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2002/gonzalez2002semantic.pdf DO - KW - informationsportale KW - information KW - project KW - Semantic KW - ontologies KW - semiport KW - portals KW - semantic KW - 2002 KW - bmbf KW - myown KW - portal KW - web L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Schmitz, C. AU - Staab, S. AU - Studer, R. AU - Stumme, G. AU - Tane, J. A2 - Driscoll, M. A2 - Reeves, T.C. T1 - Accessing Distributed Learning Repositories through a Courseware

Watchdog T2 - Proc. of E-Learning 2002 World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education on (E-Learning 2002) PB - C1 - Norfolk PY - 2002/ CY - VL - AACE IS - SP - 909 EP - 915 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2002/E-Learn02.pdf DO - KW - ontologies KW - OntologyHandbook KW - courseware KW - semantic KW - crawler KW - myown KW - watchdog KW - web KW - p2p KW - 2002 KW - FCA KW - fca KW - edutella L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Stumme, G. AU - Berendt, B. AU - Hotho, A. A2 - T1 - Usage Mining for and on the Semantic Web T2 - Proc. NSF Workshop on Next Generation Data Mining PB - C1 - Baltimore PY - 2002/november CY - VL - IS - SP - 77 EP - 86 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2002/NSF-NGDM02.pdf DO - KW - semantic KW - 2002 KW - myown KW - mining KW - usage KW - web L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Hereth, J. AU - Stumme, G. A2 - Delugach, H. S. A2 - Stumme, G. T1 - Reverse Pivoting in Conceptual Information Systems T2 - Conceptual Structures: Broadening the Base PB - Springer C1 - PY - 2001/ CY - VL - 2120 IS - SP - 202 EP - 215 UR - DO - KW - OntologyHandbook KW - FCA L1 - SN - 3-540-42344-3 N1 - fca N1 - AB - ER - TY - CONF AU - Stumme, G. AU - Taouil, R. AU - Bastide, Y. AU - Lakhal, L. A2 - Klinkenberg, R. A2 - Rüping, S. A2 - Fick, A. A2 - Henze, N. A2 - Herzog, C. A2 - Molitor, R. A2 - Schröder, O. T1 - Conceptual Clustering with Iceberg Concept Lattices T2 - Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01) PB - C1 - Universität Dortmund 763 PY - 2001/october CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2001/FGML01.pdf DO - KW - concept KW - iceberg KW - discovery KW - analysis KW - kdd KW - lattices KW - knowledge KW - formal KW - closed KW - conceptual KW - clustering KW - itemsets KW - 2001 KW - fca L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Stumme, G. AU - Taouil, R. AU - Bastide, Y. AU - Pasquier, N. AU - Lakhal, L. A2 - Baader, F. A2 - Brewker, G. A2 - Eiter, T. T1 - Intelligent Structuring and Reducing of Association Rules and with Formal Concept Analysis T2 - KI 2001: Advances in Artificial Intelligence. KI 2001 PB - Springer C1 - Heidelberg PY - 2001/ CY - VL - 2174 IS - SP - 335 EP - 350 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2001/KI01.pdf DO - KW - concept KW - discovery KW - association KW - OntologyHandbook KW - bases KW - analysis KW - kdd KW - myown KW - rule KW - knowledge KW - closed KW - formal KW - rules KW - condensed KW - FCA KW - itemsets KW - 2001 KW - fca KW - representations KW - mining L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Bastide, Y. AU - Pasquier, N. AU - Taouil, R. AU - Stumme, G. AU - Lakhal, L. A2 - Lloyd, J. A2 - Dahl, V. A2 - Furbach, U. A2 - Kerber, M. A2 - Laus, K.-K. A2 - Palamidessi, C. A2 - Pereira, L.M. A2 - Sagiv, Y. A2 - Stuckey, P.J. T1 - Mining Minimal Non-Redundant Association Rules Using Frequent Closed Itemsets T2 - Computational Logic --- CL 2000 Proc. CL'00 PB - Springer C1 - Heidelberg PY - 2000/ CY - VL - 1861 IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2000/DOOD00.pdf DO - KW - concept KW - discovery KW - analys KW - association KW - kdd KW - myown KW - frequent KW - rule KW - data KW - knowledge KW - closed KW - formal KW - rules KW - representation KW - condensed KW - itemsets KW - fca KW - 2000 KW - representations KW - mining L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Stumme, G. AU - Studer, R. AU - Sure, Y. A2 - Bodendorf, F. A2 - Grauer, M. T1 - Towards an Order-Theoretical Foundation for Maintaining and Merging Ontologies T2 - Verbundtagung Wirtschaftsinformatik 2000 PB - Shaker C1 - Aachen PY - 2000/ CY - VL - IS - SP - 136 EP - 149 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2000/REFMOD00.pdf DO - KW - concept KW - formal KW - ontologies KW - merging KW - semantic KW - analysis KW - 2000 KW - fca KW - myown KW - ontology KW - web L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Prediger, S. AU - Stumme, G. A2 - et al, E. Franconi T1 - Theory-Driven Logical Scaling T2 - Proc. 6th Intl. Workshop Knowledge Representation Meets Databases (KRDB'99) PB - C1 - PY - 1999/ CY - VL - CEUR Workshop Proc. 21 IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/1999/KRDB99.pdf DO - KW - 1999 KW - conceptual KW - concept KW - formal KW - analysis KW - fca KW - myown KW - scaling KW - lattices L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Skorsky, M. AU - Stumme, G. AU - Wille, R. AU - Wille, U. A2 - Puppe, F. A2 - Fensel, D. A2 - Kühler, J. A2 - Studer, R. A2 - Wetter, Th. T1 - Reuse in the Development Process of TOSCANA Systems T2 - Proc. Workshop on Knowledge Management, Organizational Memory and Reuse, 5th German Conf. on PB - C1 - Würzburg PY - 1999/march 3-5, CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/1999/XPS99.pdf DO - KW - 1999 KW - concept KW - formal KW - toscana KW - analysis KW - fca KW - myown L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - RPRT AU - Stumme, G. A2 - T1 - Conceptual Knowledge Discovery with Frequent Concept Lattices PB - TU Darmstadt AD - PY - 1999/ VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/1999/P2043.pdf DO - KW - concept KW - iceberg KW - discovery KW - association KW - analysis KW - kdd KW - myown KW - frequent KW - rule KW - data KW - lattices KW - knowledge KW - 1999 KW - closed KW - formal KW - rules KW - condensed KW - itemsets KW - fca KW - representations KW - mining L1 - N1 - Publications of Gerd Stumme N1 - FB4-Preprint 2043 N1 - AB - ER -