<rdf:RDF xmlns:community="http://www.bibsonomy.org/ontologies/2008/05/community#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:admin="http://webns.net/mvcb/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:cc="http://web.resource.org/cc/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:swrc="http://swrc.ontoware.org/ontology#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xml:base="http://www.bibsonomy.org/user/stumme/aggregation"><owl:Ontology rdf:about=""><rdfs:comment>BibSonomy publications for /user/stumme/aggregation</rdfs:comment><owl:imports rdf:resource="http://swrc.ontoware.org/ontology/portal"/></owl:Ontology><rdf:Description rdf:about="http://www.bibsonomy.org/bibtex/21788c88e04112a4491f19dfffb8dc39e/stumme"><owl:sameAs rdf:resource="http://www.bibsonomy.org/uri/bibtex/21788c88e04112a4491f19dfffb8dc39e/stumme"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#InProceedings"/><owl:sameAs rdf:resource="http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf"/><swrc:date>Wed Sep 20 18:23:30 CEST 2006</swrc:date><swrc:address>Heidelberg</swrc:address><swrc:booktitle>The Semantic Web: Research and Applications</swrc:booktitle><swrc:pages>530-544</swrc:pages><swrc:publisher><swrc:Organization swrc:name="Springer"/></swrc:publisher><swrc:series>LNAI</swrc:series><swrc:title>Content Aggregation on Knowledge Bases using Graph Clustering</swrc:title><swrc:volume>4011</swrc:volume><swrc:year>2006</swrc:year><swrc:keywords>2006 aggregation clustering content graph itegpub l3s myown nepomuk ontologies ontology seminar2006 theory </swrc:keywords><swrc: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.</swrc:abstract><swrc:author><rdf:Seq><rdf:_1><swrc:Person swrc:name="Christoph Schmitz"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Andreas Hotho"/></rdf:_2><rdf:_3><swrc:Person swrc:name="Robert Jäschke"/></rdf:_3><rdf:_4><swrc:Person swrc:name="Gerd Stumme"/></rdf:_4></rdf:Seq></swrc:author><swrc:editor><rdf:Seq><rdf:_1><swrc:Person swrc:name="York Sure"/></rdf:_1><rdf:_2><swrc:Person swrc:name="John Domingue"/></rdf:_2></rdf:Seq></swrc:editor></rdf:Description></rdf:RDF>
