PUMA publications for /tag/clustering%20myownhttps://puma.uni-kassel.de/tag/clustering%20myownPUMA RSS feed for /tag/clustering%20myown2024-03-29T08:04:20+01:00Content Aggregation on Knowledge Bases using Graph Clusteringhttps://puma.uni-kassel.de/bibtex/21788c88e04112a4491f19dfffb8dc39e/itegiteg2011-11-22T10:26:32+01:002006 aggregation clustering content graph itegpub l3s myown nepomuk ontologies ontology seminar2006 theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Schmitz" itemprop="url" href="/author/Christoph%20Schmitz"><span itemprop="name">C. Schmitz</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">The Semantic Web: Research and Applications</span>, </em></span><em>Volume 4011 von LNAI, </em><em>Seite <span itemprop="pagination">530-544</span>. </em><em>Heidelberg, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Tue Nov 22 10:26:32 CET 2011HeidelbergThe Semantic Web: Research and Applications530-544LNAIContent Aggregation on Knowledge Bases using Graph Clustering401120062006 aggregation clustering content graph itegpub l3s myown nepomuk ontologies ontology seminar2006 theory 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.Conceptual Clustering of Social Bookmark Siteshttps://puma.uni-kassel.de/bibtex/26d5188d66564fe4ed7386e28868504de/itegiteg2011-11-22T10:26:32+01:002007 Social bookmark bookmarking clustering collaborative conceptual folksonomies folksonomy itegpub myown social tagging tagorapub <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Miranda Grahl" itemprop="url" href="/author/Miranda%20Grahl"><span itemprop="name">M. Grahl</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)</span>, </em></span><em>Seite <span itemprop="pagination">50-54</span>. </em><em><span itemprop="publisher">Martin-Luther-Universität Halle-Wittenberg</span>, </em>(<em><span>September 2007<meta content="September 2007" itemprop="datePublished"/></span></em>)Tue Nov 22 10:26:32 CET 2011Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)sep50-54Conceptual Clustering of Social Bookmark Sites20072007 Social bookmark bookmarking clustering collaborative conceptual folksonomies folksonomy itegpub myown social tagging tagorapub Do German physicians want electronic health services? A characterization of potential adopters and rejectors in German ambulatory carehttps://puma.uni-kassel.de/bibtex/26d8d3744dda9624c4ae1b10fed7b2e3e/itegiteg2011-11-22T10:26:32+01:00Adoption Ambulatory Care Clustering Data Electronic Equipment Health Infrastructure Practice Security Services Standardization Technology Telematics itegpub myown pub_jml <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Duennebeil" itemprop="url" href="/author/S.%20Duennebeil"><span itemprop="name">S. Duennebeil</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Sunyaev" itemprop="url" href="/author/A.%20Sunyaev"><span itemprop="name">A. Sunyaev</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="I. Blohm" itemprop="url" href="/author/I.%20Blohm"><span itemprop="name">I. Blohm</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J. M. Leimeister" itemprop="url" href="/author/J.%20M.%20Leimeister"><span itemprop="name">J. Leimeister</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="H. Krcmar" itemprop="url" href="/author/H.%20Krcmar"><span itemprop="name">H. Krcmar</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">3. International Conference on Health Informatics (HealthInf) 2010</span>, </em></span><em>Valencia, Spain, </em>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)<em>163 (11-10).</em>Tue Nov 22 10:26:32 CET 2011Valencia, Spain3. International Conference on Health Informatics (HealthInf) 2010163 (11-10)Do German physicians want electronic health services? A characterization of potential adopters and rejectors in German ambulatory care2010Adoption Ambulatory Care Clustering Data Electronic Equipment Health Infrastructure Practice Security Services Standardization Technology Telematics itegpub myown pub_jml Ontologies improve text document clusteringhttps://puma.uni-kassel.de/bibtex/257a39c81cff1982dbefed529be934bee/stummestumme2010-04-07T13:54:41+02:002003 clustering data kdd mining myown ontologies text <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Staab" itemprop="url" href="/author/Steffen%20Staab"><span itemprop="name">S. Staab</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 2003 IEEE International Conference on Data Mining</span>, </em></span><em>Seite <span itemprop="pagination">541-544 (Poster</span>. </em><em>Melbourne, Florida, </em><em><span itemprop="publisher">IEEE Computer Society</span>, </em>(<em><span>November 2003<meta content="November 2003" itemprop="datePublished"/></span></em>)Wed Apr 07 13:54:41 CEST 2010Melbourne, FloridaProceedings of the 2003 IEEE International Conference on Data MiningNovember 19-22,541-544 (PosterOntologies improve text document clustering20032003 clustering data kdd mining myown ontologies text Publications of Gerd StummeText Clustering Based on Background Knowledgehttps://puma.uni-kassel.de/bibtex/261d58db419af0dbc3681432588219c3d/stummestumme2010-04-07T13:54:41+02:002003 analysis background clustering concept fca formal knowledge myown ontologies semantic text web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Staab" itemprop="url" href="/author/Steffen%20Staab"><span itemprop="name">S. Staab</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><em><span itemprop="educationalUse">Technical Report</span>, </em><em>Volume 425. </em><em><span itemprop="producer">University of Karlsruhe, Institute AIFB</span>, </em>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Wed Apr 07 13:54:41 CEST 2010Text Clustering Based on Background KnowledgeTechnical Report 42520032003 analysis background clustering concept fca formal knowledge myown ontologies semantic text web 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.Publications of Gerd StummeConceptual Clustering of Text Clustershttps://puma.uni-kassel.de/bibtex/2e253c44552a046fe90236274bcfeab13/stummestumme2010-04-07T13:54:41+02:002002 analysis clustering concept conceptual fca formal myown text <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Hotho" itemprop="url" href="/author/A.%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G. Stumme" itemprop="url" href="/author/G.%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002)</span>, </em></span><em>Seite <span itemprop="pagination">37-45</span>. </em>(<em><span>2002<meta content="2002" itemprop="datePublished"/></span></em>)Wed Apr 07 13:54:41 CEST 2010Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002)37-45Conceptual Clustering of Text Clusters20022002 analysis clustering concept conceptual fca formal myown text Publications of Gerd StummeWordnet improves text document clusteringhttps://puma.uni-kassel.de/bibtex/204c7d86337d68e4ed9ae637029c43414/stummestumme2010-04-07T13:54:41+02:002003 clustering data discovery document information ir kdd kmeans knowledge mining myown retrieval text wordnet <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A Hotho" itemprop="url" href="/author/A%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Staab" itemprop="url" href="/author/S.%20Staab"><span itemprop="name">S. Staab</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G. Stumme" itemprop="url" href="/author/G.%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. SIGIR Semantic Web Workshop</span>, </em></span><em>Toronto, </em>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Wed Apr 07 13:54:41 CEST 2010TorontoProc. SIGIR Semantic Web WorkshopWordnet improves text document clustering20032003 clustering data discovery document information ir kdd kmeans knowledge mining myown retrieval text wordnet Publications of Gerd StummeExplaining Text Clustering Results using Semantic Structureshttps://puma.uni-kassel.de/bibtex/253a943b6be4b34cf4e5329d0b58e99f6/stummestumme2010-04-07T13:54:41+02:002003 analysis clustering concept fca formal myown ontologies semantic semantics text <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Staab" itemprop="url" href="/author/Steffen%20Staab"><span itemprop="name">S. Staab</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases</span>, </em></span><em>Volume 2838 von LNAI, </em><em>Seite <span itemprop="pagination">217-228</span>. </em><em>Heidelberg, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Wed Apr 07 13:54:41 CEST 2010HeidelbergKnowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases217-228LNAIExplaining Text Clustering Results using Semantic Structures283820032003 analysis clustering concept fca formal myown ontologies semantic semantics text 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.Publications of Gerd StummeConceptual Clustering of Social Bookmark Siteshttps://puma.uni-kassel.de/bibtex/26d5188d66564fe4ed7386e28868504de/stummestumme2010-04-07T13:54:41+02:002007 Social bookmark bookmarking clustering collaborative conceptual folksonomies folksonomy itegpub myown social tagging tagorapub <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Miranda Grahl" itemprop="url" href="/author/Miranda%20Grahl"><span itemprop="name">M. Grahl</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)</span>, </em></span><em>Seite <span itemprop="pagination">50-54</span>. </em><em><span itemprop="publisher">Martin-Luther-Universität Halle-Wittenberg</span>, </em>(<em><span>September 2007<meta content="September 2007" itemprop="datePublished"/></span></em>)Wed Apr 07 13:54:41 CEST 2010Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)sep50-54Conceptual Clustering of Social Bookmark Sites20072007 Social bookmark bookmarking clustering collaborative conceptual folksonomies folksonomy itegpub myown social tagging tagorapub Publications of Gerd StummeConceptual Clustering with Iceberg Concept Latticeshttps://puma.uni-kassel.de/bibtex/2f4ec21d5f63dbc213a3a6eae076c4b62/stummestumme2010-04-07T13:54:41+02:002001 analysis closed clustering concept conceptual discovery fca formal iceberg itemsets kdd knowledge lattices myown <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G. Stumme" itemprop="url" href="/author/G.%20Stumme"><span itemprop="name">G. Stumme</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Taouil" itemprop="url" href="/author/R.%20Taouil"><span itemprop="name">R. Taouil</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Y. Bastide" itemprop="url" href="/author/Y.%20Bastide"><span itemprop="name">Y. Bastide</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="L. Lakhal" itemprop="url" href="/author/L.%20Lakhal"><span itemprop="name">L. Lakhal</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)</span>, </em></span><em>Universität Dortmund 763, </em>(<em><span>Oktober 2001<meta content="Oktober 2001" itemprop="datePublished"/></span></em>)Wed Apr 07 13:54:41 CEST 2010Universität Dortmund 763Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)OctoberConceptual Clustering with Iceberg Concept Lattices20012001 analysis closed clustering concept conceptual discovery fca formal iceberg itemsets kdd knowledge lattices myown Publications of Gerd StummeContent Aggregation on Knowledge Bases using Graph Clusteringhttps://puma.uni-kassel.de/bibtex/21788c88e04112a4491f19dfffb8dc39e/jaeschkejaeschke2009-05-25T18:35:12+02:002006 aggregation clustering graph iccs_example knowledge l3s myown trias_example <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Schmitz" itemprop="url" href="/author/Christoph%20Schmitz"><span itemprop="name">C. Schmitz</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">The Semantic Web: Research and Applications</span>, </em></span><em>Volume 4011 von Lecture Notes in Computer Science, </em><em>Seite <span itemprop="pagination">530--544</span>. </em><em>Berlin/Heidelberg, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>Juni 2006<meta content="Juni 2006" itemprop="datePublished"/></span></em>)Mon May 25 18:35:12 CEST 2009Berlin/HeidelbergThe Semantic Web: Research and Applicationsjun530--544Lecture Notes in Computer ScienceContent Aggregation on Knowledge Bases using Graph Clustering401120062006 aggregation clustering graph iccs_example knowledge l3s myown trias_example 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.Learning Ontologies to Improve Text Clustering and Classificationhttps://puma.uni-kassel.de/bibtex/2bc1d40cf4fd64780ecf712b1e40f31de/hothohotho2009-02-17T10:34:43+01:002006 classification clustering myown ol text <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Bloehdorn" itemprop="url" href="/author/Stephan%20Bloehdorn"><span itemprop="name">S. Bloehdorn</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Philipp Cimiano" itemprop="url" href="/author/Philipp%20Cimiano"><span itemprop="name">P. Cimiano</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">From Data and Information Analysis to Knowledge Engineering</span>, </em><em><span itemprop="publisher">Springer Berlin Heidelberg</span>, </em></span>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Tue Feb 17 10:34:43 CET 2009From Data and Information Analysis to Knowledge Engineering334--341Learning Ontologies to Improve Text Clustering and Classification20062006 classification clustering myown ol text 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 -SpringerLink - Book ChapterConceptual Clustering of Social Bookmarking Siteshttps://puma.uni-kassel.de/bibtex/2334d3ab11400c4a3ea3ed5b1e95c1855/hothohotho2008-02-01T09:28:11+01:002007 bookmarking clustering conceptual folksonomy kdubiq myown social sosbuch summerschool tagging taggingsurvey <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Miranda Grahl" itemprop="url" href="/author/Miranda%20Grahl"><span itemprop="name">M. Grahl</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">7th International Conference on Knowledge Management (I-KNOW '07)</span>, </em></span><em>Seite <span itemprop="pagination">356-364</span>. </em><em>Graz, Austria, </em><em><span itemprop="publisher">Know-Center</span>, </em>(<em><span>September 2007<meta content="September 2007" itemprop="datePublished"/></span></em>)Fri Feb 01 09:28:11 CET 2008Graz, Austria7th International Conference on Knowledge Management (I-KNOW '07)SEP356-364Conceptual Clustering of Social Bookmarking Sites20072007 bookmarking clustering conceptual folksonomy kdubiq myown social sosbuch summerschool tagging taggingsurvey 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.Conceptual Clustering of Social Bookmark Siteshttps://puma.uni-kassel.de/bibtex/26d5188d66564fe4ed7386e28868504de/hothohotho2008-01-31T12:22:52+01:002007 bookmarking clustering collaborative folksonomy myown social <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Miranda Grahl" itemprop="url" href="/author/Miranda%20Grahl"><span itemprop="name">M. Grahl</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)</span>, </em></span><em>Seite <span itemprop="pagination">50-54</span>. </em><em><span itemprop="publisher">Martin-Luther-Universität Halle-Wittenberg</span>, </em>(<em><span>September 2007<meta content="September 2007" itemprop="datePublished"/></span></em>)Thu Jan 31 12:22:52 CET 2008Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep50-54Conceptual Clustering of Social Bookmark Sites20072007 bookmarking clustering collaborative folksonomy myown social Text Clustering Based on Good Aggregationshttps://puma.uni-kassel.de/bibtex/2a6803e87c5145d5f55d7bb1bab8dfd67/hothohotho2008-01-28T15:00:01+01:002001 clustering gruppenbildung kmeans myown ontology text tm <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexander Maedche" itemprop="url" href="/author/Alexander%20Maedche"><span itemprop="name">A. Maedche</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Staab" itemprop="url" href="/author/Steffen%20Staab"><span itemprop="name">S. Staab</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining</span>, </em></span><em>Seite <span itemprop="pagination">607--608</span>. </em><em>Washington, DC, USA, </em><em><span itemprop="publisher">IEEE Computer Society</span>, </em>(<em><span>2001<meta content="2001" itemprop="datePublished"/></span></em>)Mon Jan 28 15:00:01 CET 2008Washington, DC, USAICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining607--608Text Clustering Based on Good Aggregations20012001 clustering gruppenbildung kmeans myown ontology text tm Text Clustering Based on Good AggregationsConceptual Clustering of Text Clustershttps://puma.uni-kassel.de/bibtex/218fdbebb76d48feccf2dceed23f4cd74/hothohotho2008-01-15T11:04:28+01:002002 clustering myown ontology text <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Hotho" itemprop="url" href="/author/A.%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G. Stumme" itemprop="url" href="/author/G.%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of FGML Workshop</span>, </em></span><em>Seite <span itemprop="pagination">37-45</span>. </em><em><span itemprop="publisher">Special Interest Group of German Informatics Society (FGML --- Fachgruppe Maschinelles Lernen der GI e.V.)</span>, </em>(<em><span>2002<meta content="2002" itemprop="datePublished"/></span></em>)Tue Jan 15 11:04:28 CET 2008Proceedings of FGML Workshop37-45Conceptual Clustering of Text Clusters20022002 clustering myown ontology text Content Aggregation on Knowledge Bases using Graph Clusteringhttps://puma.uni-kassel.de/bibtex/29a06428ec3bd72e3ea6c7a8f08e2bb85/hothohotho2008-01-15T10:33:14+01:002006 aggregation clustering content graph myown ontology theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Schmitz" itemprop="url" href="/author/Christoph%20Schmitz"><span itemprop="name">C. Schmitz</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%5c%22aschke"><span itemprop="name">R. Jäschke</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 3rd European Semantic Web Conference</span>, </em></span><em>Volume 4011 von LNCS, </em><em>Seite <span itemprop="pagination">530-544</span>. </em><em>Budva, Montenegro, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>Juni 2006<meta content="Juni 2006" itemprop="datePublished"/></span></em>)Tue Jan 15 10:33:14 CET 2008Budva, MontenegroProceedings of the 3rd European Semantic Web ConferenceJune530-544LNCSContent Aggregation on Knowledge Bases using Graph Clustering401120062006 aggregation clustering content graph myown ontology theory Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Texthttps://puma.uni-kassel.de/bibtex/248d35aa9a4d727e221c90f959462b7b2/hothohotho2008-01-15T10:26:42+01:002004 clustering learning myown taxonomies <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Philipp Cimiano" itemprop="url" href="/author/Philipp%20Cimiano"><span itemprop="name">P. Cimiano</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Staab" itemprop="url" href="/author/Steffen%20Staab"><span itemprop="name">S. Staab</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the European Conference on Artificial Intelligence (ECAI'04)</span>, </em></span><em>Seite <span itemprop="pagination">435-439</span>. </em><em>Valencia, Spain, </em><em><span itemprop="publisher">IOS Press</span>, </em>(<em><span>2004<meta content="2004" itemprop="datePublished"/></span></em>)Tue Jan 15 10:26:42 CET 2008Valencia, SpainProceedings of the European Conference on Artificial Intelligence (ECAI'04)435-439Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text20042004 clustering learning myown taxonomies Clustering Ontologies from Texthttps://puma.uni-kassel.de/bibtex/23bc6e5a51dba862da1b7b3b6ac563370/hothohotho2008-01-15T10:15:04+01:002004 clustering myown ol ontology text <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Philipp Cimiano" itemprop="url" href="/author/Philipp%20Cimiano"><span itemprop="name">P. Cimiano</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Staab" itemprop="url" href="/author/Steffen%20Staab"><span itemprop="name">S. Staab</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the Conference on Languages Resources and Evaluation (LREC)</span>, </em></span><em>Lisbon, Portugal, </em><em><span itemprop="publisher">ELRA - European Language Ressources Association</span>, </em>(<em><span>Mai 2004<meta content="Mai 2004" itemprop="datePublished"/></span></em>)Tue Jan 15 10:15:04 CET 2008Lisbon, PortugalProceedings of the Conference on Languages Resources and Evaluation (LREC)MAYClustering Ontologies from Text20042004 clustering myown ol ontology text Explaining Text Clustering Results using Semantic Structureshttps://puma.uni-kassel.de/bibtex/2c1bb26aa5d4801542f832ffab70c82e5/hothohotho2008-01-15T09:57:54+01:002003 SumSchool06 clustering fca myown text visualization <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Hotho" itemprop="url" href="/author/A.%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Staab" itemprop="url" href="/author/S.%20Staab"><span itemprop="name">S. Staab</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G. Stumme" itemprop="url" href="/author/G.%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD</span>, </em></span><em>Volume 2838 von LNCS, </em><em>Seite <span itemprop="pagination">217-228</span>. </em>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Tue Jan 15 09:57:54 CET 2008Proc. of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD217-228LNCSExplaining Text Clustering Results using Semantic Structures283820032003 SumSchool06 clustering fca myown text visualization