Publikationen

Stumme, G.; Berendt, B. & Hotho, A. (2002): Usage Mining for and on the Semantic Web. In: Proc. NSF Workshop on Next Generation Data Mining, Baltimore. [Volltext]

2000

Stumme, G.; Studer, R. & Sure, Y. (2000): Towards an Order-Theoretical Foundation for Maintaining and Merging Ontologies. In: Verbundtagung Wirtschaftsinformatik 2000, Aachen. [Volltext]

1999

Prediger, S. & Stumme, G. (1999): Theory-Driven Logical Scaling. In: Proc. 6th Intl. Workshop Knowledge Representation Meets Databases (KRDB'99), [Volltext]

2010

Benz, D.; Hotho, A.; Jaschke, R.; Krause, B.; Mitzlaff, F.; Schmitz, C. & Stumme, G. (2010): The social bookmark and publication management system bibsonomy A platform for evaluating and demonstrating Web 2.0 research. In: VLDB JOURNAL, Ausgabe/Number: 6, Vol. 19, Erscheinungsjahr/Year: 2010.

2002

Gonzalez-Olalla, J. & Stumme, G. (2002): Semantic Methods and Tools for Information Portals - The SemIPort Project (Project Description). In: Semantic Web Mining. Proc. of the Semantic Web Mining Workshop of the 13th Europ. Conf., Helsinki. [Volltext]

2001

Hereth, J. & Stumme, G. (2001): Reverse Pivoting in Conceptual Information Systems.. In: Conceptual Structures: Broadening the Base. , Heidelberg. [Volltext]

Hereth, J. & Stumme, G. (2001): Reverse Pivoting in Conceptual Information Systems. In: Conceptual Structures: Broadening the Base,

1999

Skorsky, M.; Stumme, G.; Wille, R. & Wille, U. (1999): Reuse in the Development Process of TOSCANA Systems. In: Proc. Workshop on Knowledge Management, Organizational Memory and Reuse, 5th German Conf. on, Würzburg. [Volltext]

2012

Balby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G. & Symeonidis, P. (Hrsg.) (2012): Recommender Systems for Social Tagging Systems. Erscheinungsjahr/Year: 2012. Verlag/Publisher: Springer, [Volltext]

@book{balbymarinho2012recommender,
  author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
  title = {Recommender Systems for Social Tagging Systems},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  publisher = {Springer},
  year = {2012},
  url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
  doi = {10.1007/978-1-4614-1894-8},
  isbn = {978-1-4614-1893-1},
  keywords = {recommender, social_tagging, baarbeit, toread},
  abstract = {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.}
  }

@book{balbymarinho2012recommender,
  author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
  title = {Recommender Systems for Social Tagging Systems},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  publisher = {Springer},
  year = {2012},
  url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
  doi = {10.1007/978-1-4614-1894-8},
  isbn = {978-1-4614-1893-1},
  keywords = {tagging, itegpub, recommender, collaborative, social, l3s, folksonomy, bookmarking, myown, 2012, info20, tagging,2012},
  abstract = {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.}
  }

@book{balbymarinho2012recommender,
  author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
  title = {Recommender Systems for Social Tagging Systems},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  publisher = {Springer},
  year = {2012},
  url = {http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1},
  isbn = {978-1-4614-1893-1},
  keywords = {tagging, recommender, collaborative, social, folksonomy, bookmarking, myown, 2012},
  abstract = {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.}
  }

@book{balbymarinho2012recommender,
  author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
  title = {Recommender Systems for Social Tagging Systems},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  publisher = {Springer},
  year = {2012},
  url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
  doi = {10.1007/978-1-4614-1894-8},
  isbn = {978-1-4614-1893-1},
  keywords = {tagging, recommender, collaborative, social, folksonomy, bookmarking, myown, 2012},
  abstract = {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.}
  }

2007

Cattuto, C.; Schmitz, C.; Baldassarri, A.; Servedio, V. D. P.; Loreto, V.; Hotho, A.; Grahl, M. & Stumme, G. (2007): Network Properties of Folksonomies. In: AI Communications, Ausgabe/Number: 4, Vol. 20, Erscheinungsjahr/Year: 2007. Seiten/Pages: 245 - 262. [Volltext]

1999

Stumme, G. (1999): Conceptual Knowledge Discovery with Frequent Concept Lattices.
[Volltext]

2001

Stumme, G.; Taouil, R.; Bastide, Y. & Lakhal, L. (2001): Conceptual Clustering with Iceberg Concept Lattices. In: Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01), Universität Dortmund 763. [Volltext]

2002

Hotho, A. & Stumme, G. (2002): Conceptual Clustering of Text Clusters. In: Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002), [Volltext]

2010

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. (2010): Academic Publication Management with PUMA - Collect, Organize and Share Publications. In: LECTURE NOTES IN COMPUTER SCIENCE, Ausgabe/Number: 6273, Erscheinungsjahr/Year: 2010.

1995

Stumme, G. & Wille, R. (1995): A geometrical heuristic for drawing concept lattices. In: Graph Drawing. Hrsg./Editors: Tamassia, R. & Tollis, I. Verlag/Publisher: Springer-Verlag, Erscheinungsjahr/Year: 1995. Seiten/Pages: 452-459.