@inproceedings{jaschke2013attribute,
  abstract = {We propose an approach for supporting attribute exploration by web information retrieval, in particular by posing appropriate queries to search engines, crowd sourcing systems, and the linked open data cloud. We discuss underlying general assumptions for this to work and the degree to which these can be taken for granted.},
  author = {Jäschke, Robert and Rudolph, Sebastian},
  booktitle = {Contributions to the 11th International Conference on Formal Concept Analysis},
  editor = {Cellier, Peggy and Distel, Felix and Ganter, Bernhard},
  interhash = {000ab7b0ae3ecd1d7d6ceb39de5c11d4},
  intrahash = {45e900e280661d775d8da949baee3747},
  month = may,
  organization = {Technische Universität Dresden},
  pages = {19--34},
  title = {Attribute Exploration on the Web},
  url = {http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-113133},
  urn = {urn:nbn:de:bsz:14-qucosa-113133},
  year = 2013
}

@inproceedings{eisterlehner2010visit,
  address = {Toronto, Canada},
  author = {Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Proceedings of the 21st ACM conference on Hypertext and hypermedia},
  interhash = {5584c4c57fcd8eb4663df8b114bcf09c},
  intrahash = {a97c4f7e80dcb666450acf697002155e},
  title = {Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy},
  year = 2010
}

@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 = {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://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
}

@article{kent95creating,
  author = {Kent, Robert E. and Neuss, Christian},
  date = {2003-02-14},
  ee = {http://dx.doi.org/10.1016/0169-7552(95)00095-X},
  interhash = {27e8127ff386c35eed428083b7a5c590},
  intrahash = {a9f6da98afa14d110a529e515e8f592b},
  journal = {Computer Networks and ISDN Systems},
  number = {1\&2},
  pages = {109-117},
  title = {Creating a Web Analysis and Visualization Environment},
  url = {http://dblp.uni-trier.de/db/journals/cn/cn28.html#KentN95},
  volume = 28,
  year = 1995
}

@techreport{hotho03textclustering,
  abstract = {Text document clustering plays an important role in providing intuitive
navigation and browsing mechanisms by organizing large amounts of information
into a small number of meaningful clusters. Standard partitional or agglomerative
clustering methods efficiently compute results to this end.
However, the bag of words representation used for these clustering methods is often
unsatisfactory as it ignores relationships between important terms that do not
co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning
has been achieved, because it is only specified extensionally. In order to
deal with the two problems, we integrate background knowledge into the process of
clustering text documents.
First, we preprocess the texts, enriching their representations by background knowledge
provided in a core ontology — in our application Wordnet. Then, we cluster
the documents by a partitional algorithm. Our experimental evaluation on Reuters
newsfeeds compares clustering results with pre-categorizations of news. In the experiments,
improvements of results by background knowledge compared to the baseline
can be shown for many interesting tasks.
Second, the clustering partitions the large number of documents to a relatively small
number of clusters, which may then be analyzed by conceptual clustering. In our approach,
we applied Formal Concept Analysis. Conceptual clustering techniques are
known to be too slow for directly clustering several hundreds of documents, but they
give an intensional account of cluster results. They allow for a concise description
of commonalities and distinctions of different clusters. With background knowledge
they even find abstractions like “food” (vs. specializations like “beef” or “corn”).
Thus, in our approach, partitional clustering reduces first the size of the problem
such that it becomes tractable for conceptual clustering, which then facilitates the
understanding of the results.},
  author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd},
  comment = {alpha},
  institution = {University of Karlsruhe, Institute AIFB},
  interhash = {0bc7c3fc1273355f45c8970a7ea58f97},
  intrahash = {61d58db419af0dbc3681432588219c3d},
  title = {Text Clustering Based on Background Knowledge},
  type = {Technical Report },
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003text.pdf},
  volume = 425,
  year = 2003
}

@incollection{stumme02using,
  address = {Heidelberg},
  author = {Stumme, G.},
  booktitle = {Wissensmanagement mit Referenzmodellen -- Konzepte für die Anwendungssystem- und Organisationsgestaltung},
  comment = {alpha},
  editor = {Becker, J. and Knackstedt, R.},
  interhash = {89b56b4b45d3c9256355080ce94045e0},
  intrahash = {8cd4f719765abd1c46d28f200327d935},
  pages = {163-174},
  publisher = {Physica},
  title = {Using Ontologies and Formal Concept Analysis for Organizing Business Knowledge},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/REFMOD01.ps},
  year = 2002
}

@inproceedings{stumme01bottom,
  address = {Seattle, WA, USA},
  author = {Stumme, G. and Maedche, A.},
  booktitle = {Proc. 17th Intl. Conf. on Artificial Intelligence (IJCAI '01)},
  comment = {alpha},
  editor = {Nebel, B.},
  interhash = {2ff2aa6492ba1857d18042fa4d57111d},
  intrahash = {cd6d6c64f79d13d7cf394d3a6b3858ba},
  pages = {225-230},
  title = {{FCA}-{M}erge: Bottom-Up Merging of Ontologies.},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/IJCAI01.pdf},
  year = 2001
}

@inproceedings{stumme00towardsanorder,
  address = {Aachen},
  author = {Stumme, G. and Studer, R. and Sure, Y.},
  booktitle = {Verbundtagung Wirtschaftsinformatik 2000},
  comment = {alpha},
  editor = {Bodendorf, F. and Grauer, M.},
  interhash = {9042498686a1b2c000cd4b0007a76bf8},
  intrahash = {83cc12ae3979a3e4108e5442b7da0b4f},
  pages = {136-149},
  publisher = {Shaker},
  title = {Towards an Order-Theoretical Foundation for Maintaining and Merging Ontologies},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/REFMOD00.pdf},
  year = 2000
}

@article{kent95creating,
  author = {Kent, Robert E. and Neuss, Christian},
  date = {2003-02-14},
  ee = {http://dx.doi.org/10.1016/0169-7552(95)00095-X},
  interhash = {27e8127ff386c35eed428083b7a5c590},
  intrahash = {c7ca420502d30b0d3ec430379da5bc18},
  journal = {Computer Networks and ISDN Systems},
  number = {1&2},
  pages = {109-117},
  title = {Creating a Web Analysis and Visualization Environment.},
  url = {http://dblp.uni-trier.de/db/journals/cn/cn28.html#KentN95},
  volume = 28,
  year = 1995
}