%0 
%0 Conference Proceedings
%A Duennebeil, S.; Sunyaev, A.; Blohm, I.; Leimeister, J. M. & Krcmar, H.
%D 2010
%T Do German physicians want electronic health services? A characterization of potential adopters and rejectors in German ambulatory care
%E 
%B 3. International Conference on Health Informatics (HealthInf) 2010
%C Valencia, Spain
%I 
%V 
%6
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%F ls_leimeister
%K Adoption, Ambulatory, Care, Clustering, Data, Electronic, Equipment, Health, Infrastructure, Practice, Security, Services, Standardization, Technology, Telematics, itegpub, myown, pub_jml
%X 
%Z 163 (11-10)
%U http://www.uni-kassel.de/fb7/ibwl/leimeister/pub/JML_150.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Grahl, Miranda; Hotho, Andreas & Stumme, Gerd
%D 2007
%T Conceptual Clustering of Social Bookmark Sites
%E Hinneburg, Alexander
%B Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)
%C 
%I Martin-Luther-Universität Halle-Wittenberg
%V 
%6
%N 
%P 50-54
%& 
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%S 
%7
%8 September
%9
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%4 
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%F grahl07conceptualKdml
%K 2007, Social, bookmark, bookmarking, clustering, collaborative, conceptual, folksonomies, folksonomy, itegpub, myown, social, tagging, tagorapub
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf
%+
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%0 
%0 Conference Proceedings
%A Grahl, Miranda; Hotho, Andreas & Stumme, Gerd
%D 2007
%T Conceptual Clustering of Social Bookmark Sites
%E Hinneburg, Alexander
%B Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)
%C 
%I Martin-Luther-Universität Halle-Wittenberg
%V 
%6
%N 
%P 50-54
%& 
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%7
%8 September
%9
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%3 inproceedings
%4 
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%$ 
%F grahl07conceptualKdml
%K 2007, Social, bookmark, bookmarking, clustering, collaborative, conceptual, folksonomies, folksonomy, itegpub, myown, social, tagging, tagorapub
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf
%+
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%0 
%0 Conference Proceedings
%A Grahl, Miranda; Hotho, Andreas & Stumme, Gerd
%D 2007
%T Conceptual Clustering of Social Bookmark Sites
%E Hinneburg, Alexander
%B Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)
%C 
%I Martin-Luther-Universität Halle-Wittenberg
%V 
%6
%N 
%P 50-54
%& 
%Y 
%S 
%7
%8 September
%9
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%)
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%4 
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%F grahl07conceptualKdml
%K 2007, bookmarking, clustering, collaborative, folksonomy, myown, social
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Grahl, Miranda; Hotho, Andreas & Stumme, Gerd
%D 2007
%T Conceptual Clustering of Social Bookmarking Sites
%E 
%B 7th International Conference on Knowledge Management (I-KNOW '07)
%C Graz, Austria
%I Know-Center
%V 
%6
%N 
%P 356-364
%& 
%Y 
%S 
%7
%8 SEP
%9
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%4 
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%F grahl2007clustering
%K 2007, bookmarking, clustering, conceptual, folksonomy, kdubiq, myown, social, sosbuch, summerschool, tagging, taggingsurvey
%X Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.
%Z 
%U 
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%^

%0 
%0 Book Section
%A Bloehdorn, Stephan; Cimiano, Philipp & Hotho, Andreas
%D 2006
%T Learning Ontologies to Improve Text Clustering and Classification
%E 
%B From Data and Information Analysis to Knowledge Engineering
%C 
%I Springer Berlin Heidelberg
%V 
%6
%N 
%P 334--341
%& 
%Y 
%S 
%7 
%8 
%9 
%? 
%! 
%Z 
%@ 978-3-540-31313-7
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%)
%*
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%1 
%2 SpringerLink - Book Chapter
%3 incollection
%4 
%# 
%$ 
%F bloehdorn2006learning
%K 2006, classification, clustering, myown, ol, text
%X Recent work has shown improvements in text clustering and classification tasks by integrating conceptual features extracted from ontologies. In this paper we present text mining experiments in the medical domain in which the ontological structures used are acquired automatically in an unsupervised learning process from the text corpus in question. We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones. Our results show that both types of ontologies improve results on text clustering and classification tasks, whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones.
ER  -
%Z 
%U http://www.kde.cs.uni-kassel.de/hotho/pub/2006/2006-03-gfkl05-bloehdorn-etal-learning-ontologies.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert & Stumme, Gerd
%D 2006
%T Content Aggregation on Knowledge Bases using Graph Clustering
%E Sure, York & Domingue, John
%B The Semantic Web: Research and Applications
%C Heidelberg
%I Springer
%V 4011
%6
%N 
%P 530-544
%& 
%Y 
%S LNAI
%7
%8 
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%4 
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%F schmitz2006content
%K 2006, aggregation, clustering, content, graph, itegpub, l3s, myown, nepomuk, ontologies, ontology, seminar2006, theory
%X Recently, research projects such as PADLR and SWAP
  have developed tools like Edutella or Bibster, which are targeted at
  establishing peer-to-peer knowledge management (P2PKM) systems.  In
  such a system, it is necessary to obtain provide brief semantic
  descriptions of peers, so that routing algorithms or matchmaking
  processes can make decisions about which communities peers should
  belong to, or to which peers a given query should be forwarded.

  This paper provides a graph clustering technique on
  knowledge bases for that purpose. Using this clustering, we can show
  that our strategy requires up to 58% fewer queries than the
  baselines to yield full recall in a bibliographic P2PKM scenario.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert & Stumme, Gerd
%D 2006
%T Content Aggregation on Knowledge Bases using Graph Clustering
%E Sure, York & Domingue, John
%B The Semantic Web: Research and Applications
%C Berlin/Heidelberg
%I Springer
%V 4011
%6
%N 
%P 530--544
%& 
%Y 
%S Lecture Notes in Computer Science
%7
%8 June
%9
%? 
%! 
%Z 
%@ 978-3-540-34544-2
%(
%)
%*
%L
%M
%1 
%2 
%3 inproceedings
%4 
%# 
%$ 
%F schmitz2006content
%K 2006, aggregation, clustering, graph, iccs_example, knowledge, l3s, myown, trias_example
%X Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded.

This paper provides a graph clustering technique on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario.
%Z 
%U http://www.springerlink.com/content/u121v1827v286398/
%+
%^

%0 
%0 Conference Proceedings
%A Schmitz, Christoph; Hotho, Andreas; J\"aschke, Robert & Stumme, Gerd
%D 2006
%T Content Aggregation on Knowledge Bases using Graph Clustering
%E 
%B Proceedings of the 3rd European Semantic Web Conference
%C Budva, Montenegro
%I Springer
%V 4011
%6
%N 
%P 530-544
%& 
%Y 
%S LNCS
%7
%8 June
%9
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%4 
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%F schmitz2006content
%K 2006, aggregation, clustering, content, graph, myown, ontology, theory
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006sumarize_eswc.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Cimiano, Philipp; Hotho, Andreas & Staab, Steffen
%D 2004
%T Clustering Ontologies from Text
%E 
%B Proceedings of the Conference on Languages Resources and Evaluation (LREC)
%C Lisbon, Portugal
%I ELRA - European Language Ressources Association
%V 
%6
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%8 MAY
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%4 
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%F cim04a
%K 2004, clustering, myown, ol, ontology, text
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/hotho/pub/2004/lrec04.pdf
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%^

%0 
%0 Conference Proceedings
%A Cimiano, Philipp; Hotho, Andreas & Staab, Steffen
%D 2004
%T Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text
%E de M\'a,ntaras, Ramon L\'o,pez & Saitta, Lorenza
%B Proceedings of the European Conference on Artificial Intelligence (ECAI'04)
%C Valencia, Spain
%I IOS Press
%V 
%6
%N 
%P 435-439
%& 
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%4 
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%F cim04c
%K 2004, clustering, learning, myown, taxonomies
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/hotho/pub/2004/ecai04.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Hotho, A; Staab, S. & Stumme, G.
%D 2003
%T Wordnet improves text document clustering
%E 
%B Proc. SIGIR Semantic Web Workshop
%C Toronto
%I 
%V 
%6
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%F hotho03wordnet
%K 2003, clustering, data, discovery, document, information, ir, kdd, kmeans, knowledge, mining, myown, retrieval, text, wordnet
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003wordnet.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Hotho, A.; Staab, S. & Stumme, G.
%D 2003
%T Explaining Text Clustering Results using Semantic Structures
%E 
%B Proc. of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD
%C 
%I 
%V 2838
%6
%N 
%P 217-228
%& 
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%S LNCS
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%K 2003, SumSchool06, clustering, fca, myown, text, visualization
%X 
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%0 
%0 Conference Proceedings
%A Hotho, Andreas; Staab, Steffen & Stumme, Gerd
%D 2003
%T Explaining Text Clustering Results using  Semantic  Structures
%E Lavra\vc,, Nada; Gamberger, Dragan & Todorovski, Hendrik BlockeelLjupco
%B Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases
%C Heidelberg
%I Springer
%V 2838
%6
%N 
%P 217-228
%& 
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%S LNAI
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%F hotho03explaining
%K 2003, analysis, clustering, concept, fca, formal, myown, ontologies, semantic, semantics, text
%X Common text clustering techniques offer rather poor capabilities
for explaining to their users why a particular result has been
achieved. They have the disadvantage that they do not relate
semantically nearby terms and that they cannot explain how
resulting clusters are related to each other.
 In this paper, we discuss a way of integrating a large thesaurus
 and the computation of lattices of resulting clusters  into common text clustering
 in order to overcome these two problems.
As its major result, our approach achieves an explanation using an
appropriate level of granularity at the concept level as well as
an appropriate size and complexity of the explaining lattice of
resulting clusters.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Hotho, Andreas; Staab, Steffen & Stumme, Gerd
%D 2003
%T Ontologies improve text document clustering
%E 
%B Proceedings of the 2003 IEEE International Conference on Data Mining
%C Melbourne, Florida
%I IEEE {C}omputer {S}ociety
%V 
%6
%N 
%P 541-544 (Poster
%& 
%Y 
%S 
%7
%8 November 19-22,
%9
%? 
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%3 inproceedings
%4 
%# 
%$ 
%F hotho03ontologies
%K 2003, clustering, data, kdd, mining, myown, ontologies, text
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003ontologies.pdf
%+
%^

%0 
%0 Report
%A Hotho, Andreas; Staab, Steffen & Stumme, Gerd
%D 2003
%T Text Clustering Based on Background Knowledge
%E 
%B 
%C 
%I University of Karlsruhe, Institute AIFB
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9 Technical Report 
%? 
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%3 techreport
%4 
%# 
%$ 
%F hotho03textclustering
%K 2003, analysis, background, clustering, concept, fca, formal, knowledge, myown, ontologies, semantic, text, web
%X 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.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003text.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Hotho, A. & Stumme, G.
%D 2002
%T Conceptual Clustering of Text Clusters
%E K\'okai, G. & Zeidler, J.
%B Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002)
%C 
%I 
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%6
%N 
%P 37-45
%& 
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%3 inproceedings
%4 
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%$ 
%F hotho02conceptualclustering
%K 2002, analysis, clustering, concept, conceptual, fca, formal, myown, text
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2002/FGML02.pdf
%+
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%0 
%0 Conference Proceedings
%A Hotho, A. & Stumme, G.
%D 2002
%T Conceptual Clustering of Text Clusters
%E 
%B Proceedings of FGML Workshop
%C 
%I Special Interest Group of German Informatics Society (FGML ---              Fachgruppe Maschinelles Lernen der GI e.V.)
%V 
%6
%N 
%P 37-45
%& 
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%F hotho_fgml02
%K 2002, clustering, myown, ontology, text
%X 
%Z 
%U \url{http://www.aifb.uni-karlsruhe.de/WBS/aho/pub/tc_fca_2002_submit.pdf}
%+
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%0 
%0 Conference Proceedings
%A Hotho, Andreas; Maedche, Alexander & Staab, Steffen
%D 2001
%T Text Clustering Based on Good Aggregations
%E 
%B ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining
%C Washington, DC, USA
%I IEEE Computer Society
%V 
%6
%N 
%P 607--608
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
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%@ 0-7695-1119-8
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%1 
%2 Text Clustering Based on Good Aggregations
%3 inproceedings
%4 
%# 
%$ 
%F 658040
%K 2001, clustering, gruppenbildung, kmeans, myown, ontology, text, tm
%X 
%Z 
%U http://portal.acm.org/citation.cfm?id=658040
%+
%^

%0 
%0 Conference Proceedings
%A Stumme, G.; Taouil, R.; Bastide, Y. & Lakhal, L.
%D 2001
%T Conceptual  Clustering  with  Iceberg  Concept Lattices
%E Klinkenberg, R.; Rüping, S.; Fick, A.; Henze, N.; Herzog, C.; Molitor, R. & Schröder, O.
%B Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)
%C Universität Dortmund 763
%I 
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%6
%N 
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%& 
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%S 
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%8 October
%9
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%3 inproceedings
%4 
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%$ 
%F stumme01conceptualclustering
%K 2001, analysis, closed, clustering, concept, conceptual, discovery, fca, formal, iceberg, itemsets, kdd, knowledge, lattices, myown
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2001/FGML01.pdf
%+
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