@article{jaeschke2008discovering,
  abstract = {Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.},
  address = {New York},
  author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd},
  booktitle = {Semantic Web and Web 2.0},
  doi = {10.1016/j.websem.2007.11.004},
  editor = {Finin, T. and Mizoguchi, R. and Staab, S.},
  file = {jaeschke2008discovering.pdf:jaeschke2008discovering.pdf:PDF},
  groups = {public},
  interhash = {cfca594f9dbe30694bfbcdeb40dc4e88},
  intrahash = {18e8babe208fae2c0342438617b0ec31},
  issn = {1570-8268},
  journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
  journalpub = {1},
  month = feb,
  number = 1,
  pages = {38--53},
  publisher = {Elsevier},
  timestamp = {2010-11-10 15:35:25},
  title = {Discovering Shared Conceptualizations in Folksonomies},
  url = {http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b},
  username = {dbenz},
  vgwort = {59},
  volume = 6,
  year = 2008
}

@inproceedings{krause2008logsonomy,
  abstract = {Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance.  This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.},
  address = {New York, NY, USA},
  author = {Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
  booktitle = {HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia},
  doi = {10.1145/1379092.1379123},
  interhash = {6d34ea1823d95b9dbf37d4db4d125d2a},
  intrahash = {e64d14f3207766f4afc65983fa759ffe},
  isbn = {978-1-59593-985-2},
  location = {Pittsburgh, PA, USA},
  pages = {157--166},
  publisher = {ACM},
  title = {Logsonomy - Social Information Retrieval with Logdata},
  url = {http://www.kde.cs.uni-kassel.de/pub/pdf/krause2008logsonomy.pdf},
  vgwort = {17},
  year = 2008
}

@inproceedings{jaeschke2008logsonomy,
  abstract = {In social bookmarking systems users describe bookmarks
by keywords called tags. The structure behind
these social systems, called folksonomies, can be
viewed as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows specific
structural properties that explain its growth and the possibility
of serendipitous exploration.
Search engines filter the vast information of the web.
Queries describe a user’s information need. In response
to the displayed results of the search engine, users click
on the links of the result page as they expect the answer
to be of relevance. The clickdata can be represented as a
folksonomy in which queries are descriptions of clicked
URLs. This poster analyzes the topological characteristics
of the resulting tripartite hypergraph of queries,
users and bookmarks of two query logs and compares it
two a snapshot of the folksonomy del.icio.us.},
  author = {Jäschke, Robert and Krause, Beate and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)},
  interhash = {13ec3f45fc7e0364cdc6b9a7c12c5c2c},
  intrahash = {359e1eccdc524334d4a2ad51330f76ae},
  publisher = {AAAI Press},
  title = {Logsonomy — A Search Engine Folksonomy},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf},
  vgwort = {7},
  year = 2008
}

@inproceedings{krause2008logsonomy,
  abstract = {Social bookmarking systems constitute an established
part of the Web 2.0. In such systems
users describe bookmarks by keywords
called tags. The structure behind these social
systems, called folksonomies, can be viewed
as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows
specific structural properties that explain its
growth and the possibility of serendipitous
exploration.
Today’s search engines represent the gateway
to retrieve information from the World Wide
Web. Short queries typically consisting of
two to three words describe a user’s information
need. In response to the displayed
results of the search engine, users click on
the links of the result page as they expect
the answer to be of relevance.
This clickdata can be represented as a folksonomy
in which queries are descriptions of
clicked URLs. The resulting network structure,
which we will term logsonomy is very
similar to the one of folksonomies. In order
to find out about its properties, we analyze
the topological characteristics of the tripartite
hypergraph of queries, users and bookmarks
on a large snapshot of del.icio.us and
on query logs of two large search engines.
All of the three datasets show small world
properties. The tagging behavior of users,
which is explained by preferential attachment
of the tags in social bookmark systems, is
reflected in the distribution of single query
words in search engines. We can conclude
that the clicking behaviour of search engine
users based on the displayed search results
and the tagging behaviour of social bookmarking
users is driven by similar dynamics.},
  address = {New York, NY, USA},
  author = {Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
  booktitle = {HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia},
  doi = {http://doi.acm.org/10.1145/1379092.1379123},
  interhash = {6d34ea1823d95b9dbf37d4db4d125d2a},
  intrahash = {76d81124951ae39060a8bc98f4883435},
  isbn = {978-1-59593-985-2},
  location = {Pittsburgh, PA, USA},
  pages = {157--166},
  publisher = {ACM},
  title = {Logsonomy - Social Information Retrieval with Logdata},
  url = {http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia},
  vgwort = {17},
  year = 2008
}

@inproceedings{Jaeschke2008logsonomy,
  abstract = {In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us. },
  address = {Menlo Park, CA, USA},
  author = {Jäschke, Robert and Krause, Beate and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the Second International Conference on Weblogs and Social Media (ICWSM 2008)},
  interhash = {13ec3f45fc7e0364cdc6b9a7c12c5c2c},
  intrahash = {7eb26a177187ea8cf788cc897d66ee48},
  isbn = {978-1-57735-355-3},
  pages = {192--193},
  publisher = {AAAI Press},
  title = {Logsonomy -- A Search Engine Folksonomy},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf},
  vgwort = {7},
  year = 2008
}

@inproceedings{jaeschke2007tag,
  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. 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.},
  address = {Berlin, Heidelberg},
  author = {Jäschke, Robert and Balby Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
  booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases},
  editor = {Kok, Joost N. and Koronacki, Jacek and de Mántaras, Ramon López and Matwin, Stan and Mladenic, Dunja and Skowron, Andrzej},
  ee = {http://dx.doi.org/10.1007/978-3-540-74976-9_52},
  interhash = {7e212e3bac146d406035adebff248371},
  intrahash = {bb8ecec699a2f129322fe334747c6aef},
  isbn = {978-3-540-74975-2},
  pages = {506-514},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Tag Recommendations in Folksonomies},
  url = {http://dx.doi.org/10.1007/978-3-540-74976-9_52},
  vgwort = {14},
  volume = 4702,
  year = 2007
}