Huang, A.; Milne, D. N.; Frank, E. & Witten, I. H.
(2009):
Clustering Documents Using a Wikipedia-Based Concept Representation..
In: PAKDD,
[Volltext]
[BibTeX][Endnote]
@inproceedings{conf/pakdd/HuangMFW09,
author = {Huang, Anna and Milne, David N. and Frank, Eibe and Witten, Ian H.},
title = {Clustering Documents Using a Wikipedia-Based Concept Representation.},
editor = {Theeramunkong, Thanaruk and Kijsirikul, Boonserm and Cercone, Nick and Ho, Tu Bao},
booktitle = {PAKDD},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
year = {2009},
volume = {5476},
pages = {628-636},
url = {http://dblp.uni-trier.de/db/conf/pakdd/pakdd2009.html#HuangMFW09},
isbn = {978-3-642-01306-5},
keywords = {background, clustering, knowledge, ontology, tm, wikipedia}
}
%0 = inproceedings
%A = Huang, Anna and Milne, David N. and Frank, Eibe and Witten, Ian H.
%B = PAKDD
%D = 2009
%I = Springer
%T = Clustering Documents Using a Wikipedia-Based Concept Representation.
%U = http://dblp.uni-trier.de/db/conf/pakdd/pakdd2009.html#HuangMFW09
Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G.
(2006):
Content Aggregation on Knowledge Bases using Graph Clustering.
In: The Semantic Web: Research and Applications,
Heidelberg.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]
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.
@inproceedings{schmitz2006content,
author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
title = {Content Aggregation on Knowledge Bases using Graph Clustering},
editor = {Sure, York and Domingue, John},
booktitle = {The Semantic Web: Research and Applications},
series = {LNAI},
publisher = {Springer},
address = {Heidelberg},
year = {2006},
volume = {4011},
pages = {530-544},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf},
keywords = {2006, aggregation, clustering, content, graph, itegpub, l3s, myown, nepomuk, ontologies, ontology, seminar2006, theory},
abstract = {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.}
}
%0 = inproceedings
%A = Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd
%B = The Semantic Web: Research and Applications
%C = Heidelberg
%D = 2006
%I = Springer
%T = Content Aggregation on Knowledge Bases using Graph Clustering
%U = http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf
Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G.
(2006):
Content Aggregation on Knowledge Bases using Graph Clustering.
In: Proceedings of the 3rd European Semantic Web Conference,
Budva, Montenegro.
[Volltext]
[BibTeX][Endnote]
@inproceedings{schmitz2006content,
author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
title = {Content Aggregation on Knowledge Bases using Graph Clustering},
booktitle = {Proceedings of the 3rd European Semantic Web Conference},
series = {LNCS},
publisher = {Springer},
address = {Budva, Montenegro},
year = {2006},
volume = {4011},
pages = {530-544},
url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006sumarize_eswc.pdf},
isbn = {3-540-34544-2},
keywords = {2006, aggregation, clustering, content, graph, myown, ontology, theory}
}
%0 = inproceedings
%A = Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd
%B = Proceedings of the 3rd European Semantic Web Conference
%C = Budva, Montenegro
%D = 2006
%I = Springer
%T = Content Aggregation on Knowledge Bases using Graph Clustering
%U = http://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006sumarize_eswc.pdf
Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G.
(2006):
Content Aggregation on Knowledge Bases using Graph Clustering.
In: The Semantic Web: Research and Applications,
Heidelberg.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]
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.
@inproceedings{schmitz2006content,
author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
title = {Content Aggregation on Knowledge Bases using Graph Clustering},
editor = {Sure, York and Domingue, John},
booktitle = {The Semantic Web: Research and Applications},
series = {LNAI},
publisher = {Springer},
address = {Heidelberg},
year = {2006},
volume = {4011},
pages = {530-544},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf},
keywords = {2006, aggregation, clustering, content, graph, itegpub, l3s, myown, nepomuk, ontologies, ontology, seminar2006, theory},
abstract = {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.}
}
%0 = inproceedings
%A = Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd
%B = The Semantic Web: Research and Applications
%C = Heidelberg
%D = 2006
%I = Springer
%T = Content Aggregation on Knowledge Bases using Graph Clustering
%U = http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf
Schmitz, P.
(2006):
Inducing Ontology from Flickr Tags..
In: Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland,
[Volltext]
[BibTeX][Endnote]
@inproceedings{schmitz06,
author = {Schmitz, Patrick},
title = {Inducing Ontology from Flickr Tags.},
booktitle = {Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland},
year = {2006},
url = {http://www.ibiblio.org/www_tagging/2006/22.pdf},
keywords = {clustering, folksonomy, learning, ol, ontology, semantic, sosbuch, tagging, taggingsurvey, toread, webzu}
}
%0 = inproceedings
%A = Schmitz, Patrick
%B = Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland
%D = 2006
%T = Inducing Ontology from Flickr Tags.
%U = http://www.ibiblio.org/www_tagging/2006/22.pdf
Mika, P.
(2005):
Ontologies Are Us: A Unified Model of Social Networks and Semantics.
In: International Semantic Web Conference,
[Volltext]
[BibTeX][Endnote]
@inproceedings{Mika2005,
author = {Mika, Peter},
title = {Ontologies Are Us: A Unified Model of Social Networks and Semantics},
booktitle = {International Semantic Web Conference},
series = {LNCS},
publisher = {Springer},
year = {2005},
pages = {522-536},
url = {http://www.cs.vu.nl/~pmika/research/papers/ISWC-folksonomy.pdf},
doi = {10.1007/11574620_38},
keywords = {2.0, clustering, folksonomy, kdubiq, ontology, semantic, socialsoftware, sosbuch, summerschool, tagging, taggingsurvey, web}
}
%0 = inproceedings
%A = Mika, Peter
%B = International Semantic Web Conference
%D = 2005
%I = Springer
%T = Ontologies Are Us: A Unified Model of Social Networks and Semantics
%U = http://www.cs.vu.nl/~pmika/research/papers/ISWC-folksonomy.pdf
Cimiano, P.; Hotho, A. & Staab, S.
(2004):
Clustering Ontologies from Text.
In: Proceedings of the Conference on Languages Resources and Evaluation (LREC),
Lisbon, Portugal.
[Volltext]
[BibTeX][Endnote]
@inproceedings{cim04a,
author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen},
title = {Clustering Ontologies from Text},
booktitle = {Proceedings of the Conference on Languages Resources and Evaluation (LREC)},
publisher = {ELRA - European Language Ressources Association},
address = {Lisbon, Portugal},
year = {2004},
url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2004/lrec04.pdf},
keywords = {2004, clustering, myown, ol, ontology, text}
}
%0 = inproceedings
%A = Cimiano, Philipp and Hotho, Andreas and Staab, Steffen
%B = Proceedings of the Conference on Languages Resources and Evaluation (LREC)
%C = Lisbon, Portugal
%D = 2004
%I = ELRA - European Language Ressources Association
%T = Clustering Ontologies from Text
%U = http://www.kde.cs.uni-kassel.de/hotho/pub/2004/lrec04.pdf
Hotho, A.; Staab, S. & Stumme, G.
(2003):
Ontologies Improve Text Document Clustering.
In: Proc. of the ICDM 03, The 2003 IEEE International Conference on Data Mining,
[Volltext]
[BibTeX][Endnote]
@inproceedings{hotho_icdm03,
author = {Hotho, A. and Staab, S. and Stumme, G.},
title = {Ontologies Improve Text Document Clustering},
booktitle = {Proc. of the ICDM 03, The 2003 IEEE International Conference on Data Mining},
year = {2003},
pages = {541-544},
url = {http://www.kde.cs.uni-kassel.de/hotho/pub/hothoa_icdm_poster03.pdf},
keywords = {clustering, text, ontology, myown, 2003}
}
%0 = inproceedings
%A = Hotho, A. and Staab, S. and Stumme, G.
%B = Proc. of the ICDM 03, The 2003 IEEE International Conference on Data Mining
%D = 2003
%T = Ontologies Improve Text Document Clustering
%U = http://www.kde.cs.uni-kassel.de/hotho/pub/hothoa_icdm_poster03.pdf
Hotho, A.; Staab, S. & Stumme, G.
(2003):
WordNet improves text document clustering.
In: Proc. of the SIGIR 2003 Semantic Web Workshop,
Toronto, Canada.
[Volltext]
[BibTeX][Endnote]
@inproceedings{hotho_sigir03,
author = {Hotho, A. and Staab, S. and Stumme, G.},
title = {WordNet improves text document clustering},
booktitle = {Proc. of the SIGIR 2003 Semantic Web Workshop},
address = {Toronto, Canada},
year = {2003},
url = {http://www.kde.cs.uni-kassel.de/hotho/pub/hothoetal_sigir_ws_sem_web.pdf},
keywords = {clustering, text, ontology, wordnet, evaluation, myown, SumSchool06, 2003}
}
%0 = inproceedings
%A = Hotho, A. and Staab, S. and Stumme, G.
%B = Proc. of the SIGIR 2003 Semantic Web Workshop
%C = Toronto, Canada
%D = 2003
%T = WordNet improves text document clustering
%U = http://www.kde.cs.uni-kassel.de/hotho/pub/hothoetal_sigir_ws_sem_web.pdf
Hotho, A.; Staab, S. & Stumme, G.
(2003):
Explaining Text Clustering Results using Semantic Structures.
In: Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases,
Heidelberg.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]
Common text clustering techniques offer rather poor capabilities
r explaining to their users why a particular result has been
hieved. They have the disadvantage that they do not relate
mantically nearby terms and that they cannot explain how
sulting clusters are related to each other.
n this paper, we discuss a way of integrating a large thesaurus
nd the computation of lattices of resulting clusters into common text clustering
n order to overcome these two problems.
its major result, our approach achieves an explanation using an
propriate level of granularity at the concept level as well as
appropriate size and complexity of the explaining lattice of
sulting clusters.
@inproceedings{hotho03explaining,
author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd},
title = {Explaining Text Clustering Results using Semantic Structures},
editor = {Lavrač, Nada and Gamberger, Dragan and Todorovski, Hendrik BlockeelLjupco},
booktitle = {Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases},
series = {LNAI},
publisher = {Springer},
address = {Heidelberg},
year = {2003},
volume = {2838},
pages = {217-228},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf},
keywords = {clustering, concept, fca, formal, iccs_example, ontology, text, trias_example},
abstract = {Common text clustering techniques offer rather poor capabilities
r explaining to their users why a particular result has been
hieved. They have the disadvantage that they do not relate
mantically nearby terms and that they cannot explain how
sulting clusters are related to each other.
n this paper, we discuss a way of integrating a large thesaurus
nd the computation of lattices of resulting clusters into common text clustering
n order to overcome these two problems.
its major result, our approach achieves an explanation using an
propriate level of granularity at the concept level as well as
appropriate size and complexity of the explaining lattice of
sulting clusters.}
}
%0 = inproceedings
%A = Hotho, Andreas and Staab, Steffen and Stumme, Gerd
%B = Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases
%C = Heidelberg
%D = 2003
%I = Springer
%T = Explaining Text Clustering Results using Semantic Structures
%U = http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf
Hotho, A.; Staab, S. & Stumme, G.
(2003):
Ontologies improve text document clustering.
In: Proceedings of the 2003 IEEE International Conference on Data Mining,
Melbourne, Florida.
[Volltext]
[BibTeX][Endnote]
@inproceedings{hotho03ontologies,
author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd},
title = {Ontologies improve text document clustering},
booktitle = {Proceedings of the 2003 IEEE International Conference on Data Mining},
publisher = {IEEE Computer Society},
address = {Melbourne, Florida},
year = {2003},
pages = {541-544 (Poster},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003ontologies.pdf},
keywords = {clustering, iccs_example, ontology, text, trias_example}
}
%0 = inproceedings
%A = Hotho, Andreas and Staab, Steffen and Stumme, Gerd
%B = Proceedings of the 2003 IEEE International Conference on Data Mining
%C = Melbourne, Florida
%D = 2003
%I = IEEE Computer Society
%T = Ontologies improve text document clustering
%U = http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003ontologies.pdf
Staab, S. & Hotho, A.
(2003):
Ontology-based Text Document Clustering..
In: Intelligent Information Processing and Web Mining, Proceedings of the International IIS: IIPWM'03 Conference held in Zakopane,
[Volltext]
[BibTeX][Endnote]
@inproceedings{conf/iis/StaabH03,
author = {Staab, Steffen and Hotho, Andreas},
title = {Ontology-based Text Document Clustering.},
booktitle = {Intelligent Information Processing and Web Mining, Proceedings of the International IIS: IIPWM'03 Conference held in Zakopane},
year = {2003},
pages = {451-452},
url = {http://dblp.uni-trier.de/db/conf/iis/iis2003.html#StaabH03},
isbn = {3-540-00843-8},
keywords = {2003, clustering, myown, ontology, text}
}
%0 = inproceedings
%A = Staab, Steffen and Hotho, Andreas
%B = Intelligent Information Processing and Web Mining, Proceedings of the International IIS: IIPWM'03 Conference held in Zakopane
%D = 2003
%T = Ontology-based Text Document Clustering.
%U = http://dblp.uni-trier.de/db/conf/iis/iis2003.html#StaabH03
Hotho, A. & Stumme, G.
(2002):
Conceptual Clustering of Text Clusters.
In: Proceedings of FGML Workshop,
[Volltext]
[BibTeX][Endnote]
@inproceedings{hotho_fgml02,
author = {Hotho, A. and Stumme, G.},
title = {Conceptual Clustering of Text Clusters},
booktitle = {Proceedings of FGML Workshop},
publisher = {Special Interest Group of German Informatics Society (FGML --- Fachgruppe Maschinelles Lernen der GI e.V.)},
year = {2002},
pages = {37-45},
url = {http://www.aifb.uni-karlsruhe.de/WBS/aho/pub/tc_fca_2002_submit.pdf},
keywords = {2002, clustering, myown, ontology, text}
}
%0 = inproceedings
%A = Hotho, A. and Stumme, G.
%B = Proceedings of FGML Workshop
%D = 2002
%I = Special Interest Group of German Informatics Society (FGML --- Fachgruppe Maschinelles Lernen der GI e.V.)
%T = Conceptual Clustering of Text Clusters
%U = http://www.aifb.uni-karlsruhe.de/WBS/aho/pub/tc_fca_2002_submit.pdf
Hotho, A.; Maedche, A. & Staab, S.
(2002):
Text Clustering Based on Good Aggregations.
In: Künstliche Intelligenz (KI),
Ausgabe/Number: 4,
Vol. 16,
Erscheinungsjahr/Year: 2002.
Seiten/Pages: 48-54.
[Volltext] [BibTeX]
[Endnote]
@article{hotho02ki,
author = {Hotho, Andreas and Maedche, Alexander and Staab, Steffen},
title = {Text Clustering Based on Good Aggregations},
journal = {Künstliche Intelligenz (KI)},
year = {2002},
volume = {16},
number = {4},
pages = {48-54},
url = {http://www.aifb.uni-karlsruhe.de/WBS/aho/pub/Ontology_based_Text_Document_Clustering_2002.pdf},
keywords = {clustering, text, ontology, SumSchool06, myown, 2002}
}
%0 = article
%A = Hotho, Andreas and Maedche, Alexander and Staab, Steffen
%D = 2002
%T = Text Clustering Based on Good Aggregations
%U = http://www.aifb.uni-karlsruhe.de/WBS/aho/pub/Ontology_based_Text_Document_Clustering_2002.pdf
Hotho, A.; Maedche, A. & Staab, S.
(2001):
Ontology-based Text Clustering.
In: Proc. of the Workshop ``Text Learning: Beyond Supervision'' at IJCAI 2001. Seattle, WA, USA, August 6, 2001,
[BibTeX][Endnote]
@inproceedings{hotho-ijcaiws2001,
author = {Hotho, Andreas and Maedche, Alexander and Staab, Steffen},
title = {Ontology-based Text Clustering},
booktitle = {Proc. of the Workshop ``Text Learning: Beyond Supervision'' at IJCAI 2001. Seattle, WA, USA, August 6, 2001},
year = {2001},
keywords = {clustering, 2001, text, ontology, myown, SumSchool06, knowledge, background}
}
%0 = inproceedings
%A = Hotho, Andreas and Maedche, Alexander and Staab, Steffen
%B = Proc. of the Workshop ``Text Learning: Beyond Supervision'' at IJCAI 2001. Seattle, WA, USA, August 6, 2001
%D = 2001
%T = Ontology-based Text Clustering
Hotho, A.; Maedche, A. & Staab, S.
(2001):
Text Clustering Based on Good Aggregations.
In: ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining,
Washington, DC, USA.
[Volltext]
[BibTeX][Endnote]
@inproceedings{658040,
author = {Hotho, Andreas and Maedche, Alexander and Staab, Steffen},
title = {Text Clustering Based on Good Aggregations},
booktitle = {ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining},
publisher = {IEEE Computer Society},
address = {Washington, DC, USA},
year = {2001},
pages = {607--608},
url = {http://portal.acm.org/citation.cfm?id=658040},
isbn = {0-7695-1119-8},
keywords = {2001, clustering, gruppenbildung, kmeans, myown, ontology, text, tm}
}
%0 = inproceedings
%A = Hotho, Andreas and Maedche, Alexander and Staab, Steffen
%B = ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining
%C = Washington, DC, USA
%D = 2001
%I = IEEE Computer Society
%T = Text Clustering Based on Good Aggregations
%U = http://portal.acm.org/citation.cfm?id=658040