Stumme, G.; Berendt, B. & Hotho, A.: Usage Mining for and on the Semantic Web. Proc. NSF Workshop on Next Generation Data Mining. Baltimore: 2002, S. 77-86
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Stumme, G.; Studer, R. & Sure, Y.: Towards an Order-Theoretical Foundation for Maintaining and Merging Ontologies. In: Bodendorf, F. & Grauer, M. (Hrsg.): Verbundtagung Wirtschaftsinformatik 2000. Aachen: Shaker, 2000, S. 136-149
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Prediger, S. & Stumme, G.: Theory-Driven Logical Scaling. In: et al, E. F. (Hrsg.): Proc. 6th Intl. Workshop Knowledge Representation Meets Databases (KRDB'99). 1999 (CEUR Workshop Proc. 21)
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Benz, D.; Hotho, A.; Jaschke, R.; Krause, B.; Mitzlaff, F.; Schmitz, C. & Stumme, G.: The social bookmark and publication management system bibsonomy A platform for evaluating and demonstrating Web 2.0 research. In: VLDB JOURNAL 19 (2010), Nr. 6,

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.

Gonzalez-Olalla, J. & Stumme, G.: Semantic Methods and Tools for Information Portals - The SemIPort Project (Project Description). In: Berendt, B.; Hotho, A. & Stumme, G. (Hrsg.): Semantic Web Mining. Proc. of the Semantic Web Mining Workshop of the 13th Europ. Conf.. Helsinki: 2002, S. 90
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Hereth, J. & Stumme, G.: Reverse Pivoting in Conceptual Information Systems.. In: Delugach, H. & Stumme, G. (Hrsg.): Conceptual Structures: Broadening the Base. . Heidelberg: Springer, 2001 (LNAI 2120), S. 202-215
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Hereth, J. & Stumme, G.: Reverse Pivoting in Conceptual Information Systems. In: Delugach, H. S. & Stumme, G. (Hrsg.): Conceptual Structures: Broadening the Base. Springer, 2001 (Lecture Notes in Computer Science 2120), S. 202-215

Skorsky, M.; Stumme, G.; Wille, R. & Wille, U.: Reuse in the Development Process of TOSCANA Systems. In: Puppe, F.; Fensel, D.; Kühler, J.; Studer, R. & Wetter, T. (Hrsg.): Proc. Workshop on Knowledge Management, Organizational Memory and Reuse, 5th German Conf. on. Würzburg: 1999
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Mitzlaff, F. & Stumme, G.: Relatedness of given names. In: Human Journal 1 (2012), Nr. 4, S. 205-217

Balby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G. & Symeonidis, P.: Recommender Systems for Social Tagging Systems. Springer, 2012SpringerBriefs in Electrical and Computer Engineering
[Volltext]
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.

Balby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G. & Symeonidis, P.: Recommender Systems for Social Tagging Systems. Springer, 2012SpringerBriefs in Electrical and Computer Engineering
[Volltext]
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.

Balby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G. & Symeonidis, P.: Recommender Systems for Social Tagging Systems. Springer, 2012SpringerBriefs in Electrical and Computer Engineering
[Volltext]
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.

Balby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G. & Symeonidis, P.: Recommender Systems for Social Tagging Systems. Springer, 2012SpringerBriefs in Electrical and Computer Engineering
[Volltext]
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.

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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Stumme, G.: Conceptual Knowledge Discovery with Frequent Concept Lattices. , 1999
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Stumme, G.; Taouil, R.; Bastide, Y. & Lakhal, L.: Conceptual Clustering with Iceberg Concept Lattices. In: Klinkenberg, R.; Rüping, S.; Fick, A.; Henze, N.; Herzog, C.; Molitor, R. & Schröder, O. (Hrsg.): Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01). Universität Dortmund 763: 2001
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Hotho, A. & Stumme, G.: Conceptual Clustering of Text Clusters. In: Kókai, G. & Zeidler, J. (Hrsg.): Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002). 2002, S. 37-45
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Benz, D.; Hotho, A.; Jaschke, R.; Stumme, G.; Halle, A.; Lima, A. G. S.; Steenweg, H.; Stefani, S.; Lalmas, M.; Jose, J.; Rauber, A.; Sebastiani, F. & Frommholz, I.: Academic Publication Management with PUMA - Collect, Organize and Share Publications. In: LECTURE NOTES IN COMPUTER SCIENCE (2010), Nr. 6273,

Benz, D.; Hotho, A.; Jaschke, R.; Stumme, G.; Halle, A.; Lima, A. G. S.; Steenweg, H.; Stefani, S.; Lalmas, M.; Jose, J.; Rauber, A.; Sebastiani, F. & Frommholz, I.: Academic Publication Management with PUMA - Collect, Organize and Share Publications. In: LECTURE NOTES IN COMPUTER SCIENCE (2010), Nr. 6273,

Stumme, G. & Wille, R.: A geometrical heuristic for drawing concept lattices. In: Tamassia, R. & Tollis, I. (Hrsg.): Graph Drawing. Springer-Verlag, 1995Lecture Notes in Computer Science, Vol. 894 , S. 452-459