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