PUMA publications for /user/stumme/graphhttps://puma.uni-kassel.de/user/stumme/graphPUMA RSS feed for /user/stumme/graph2024-03-29T09:44:55+01:00Internet-Graphenhttps://puma.uni-kassel.de/bibtex/2783481b980117c756d4560c1640ae4a0/stummestumme2014-11-10T16:08:58+01:00Graph Graphen Informatik Informatik-Spektrum Internet Spektrum graphs <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Klaus Heidtmann" itemprop="url" href="/author/Klaus%20Heidtmann"><span itemprop="name">K. Heidtmann</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Informatik-Spektrum</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">36 </span></span>(<span itemprop="issueNumber">5</span>):
<span itemprop="pagination">440-448</span></em> </span>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Mon Nov 10 16:08:58 CET 2014Informatik-Spektrum5440-448Internet-Graphen362013Graph Graphen Informatik Informatik-Spektrum Internet Spektrum graphs Bildeten die Keimzellen des Internet noch kleine und einfach strukturierte Netze, so vergrößerten sich sowohl seine physikalischen als auch seine logischen Topologien später rasant. Wuchs einerseits das Netz aus Rechnern als Knoten und Verbindungsleitungen als Kanten immer weiter, so bedienten sich andererseits gleichzeitig immer mehr Anwendungen dieser Infrastruktur, um darüber ihrerseits immer größere und komplexere virtuelle Netze zu weben, z. B. das WWW oder soziale Online-Netze. Auf jeder Ebene dieser Hierarchie lassen sich die jeweiligen Netztopologien mithilfe von Graphen beschreiben und so mathematisch untersuchen. So ergeben sich interessante Einblicke in die Struktureigenschaften unterschiedlicher Graphentypen, die großen Einfluss auf die Leistungsfähigkeit des Internet haben. Hierzu werden charakteristische Eigenschaften und entsprechende Kenngrößen verschiedener Graphentypen betrachtet wie der Knotengrad, die Durchschnittsdistanz, die Variation der Kantendichte in unterschiedlichen Netzteilen und die topologische Robustheit als Widerstandsfähigkeit gegenüber Ausfällen und Angriffen. Es wird dabei Bezug genommen auf analytische, simulative und zahlreiche empirische Untersuchungen des Internets und hingewiesen auf Simulationsprogramme sowie Abbildungen von Internetgraphen im Internet. Internet-Graphen - SpringerDeeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendationshttps://puma.uni-kassel.de/bibtex/2e585a92994be476480545eb62d741642/stummestumme2013-12-16T17:19:49+01:002013 bookmarking collaborative folkrank folksonomy graph iteg itegpub l3s recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nikolas Landia" itemprop="url" href="/author/Nikolas%20Landia"><span itemprop="name">N. Landia</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</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>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sarabjot Singh Anand" itemprop="url" href="/author/Sarabjot%20Singh%20Anand"><span itemprop="name">S. Anand</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="Nathan Griffiths" itemprop="url" href="/author/Nathan%20Griffiths"><span itemprop="name">N. Griffiths</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>cs.IR</em></span></span> </span>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Mon Dec 16 17:19:49 CET 2013cs.IRDeeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations1310.149820132013 bookmarking collaborative folkrank folksonomy graph iteg itegpub l3s recommender social tagging The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.An analysis of tag-recommender evaluation procedureshttps://puma.uni-kassel.de/bibtex/2aa4b3d79a362d7415aaa77625b590dfa/stummestumme2013-12-16T17:19:49+01:002013 bibsonomy bookmarking collaborative core evaluation folkrank folksonomy graph iteg itegpub l3s recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, und <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>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 7th ACM conference on Recommender systems</span>, </em></span><em>Seite <span itemprop="pagination">343--346</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Mon Dec 16 17:19:49 CET 2013New York, NY, USAProceedings of the 7th ACM conference on Recommender systems343--346RecSys '13An analysis of tag-recommender evaluation procedures20132013 bibsonomy bookmarking collaborative core evaluation folkrank folksonomy graph iteg itegpub l3s recommender social tagging Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.Visualization of bibliographic networks with a reshaped landscape metaphorhttps://puma.uni-kassel.de/bibtex/2e5e72eed2d871523dc1100f060658a1c/stummestumme2011-04-19T11:22:02+02:00bibliographic bibliography citation graph networks sna <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="U. Brandes" itemprop="url" href="/author/U.%20Brandes"><span itemprop="name">U. Brandes</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="T. Willhalm" itemprop="url" href="/author/T.%20Willhalm"><span itemprop="name">T. Willhalm</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the symposium on Data Visualisation 2002</span>, </em></span><em>Seite <span itemprop="pagination">159--ff</span>. </em><em>Aire-la-Ville, Switzerland, Switzerland, </em><em><span itemprop="publisher">Eurographics Association</span>, </em>(<em><span>2002<meta content="2002" itemprop="datePublished"/></span></em>)Tue Apr 19 11:22:02 CEST 2011Aire-la-Ville, Switzerland, SwitzerlandProceedings of the symposium on Data Visualisation 2002159--ffVISSYM '02Visualization of bibliographic networks with a reshaped landscape metaphor2002bibliographic bibliography citation graph networks sna We describe a novel approach to visualize bibliographic networks that facilitates the simultaneous identification of clusters (e.g., topic areas) and prominent entities (e.g., surveys or landmark papers). While employing the landscape metaphor proposed in several earlier works, we introduce new means to determine relevant parameters of the landscape. Moreover, we are able to compute prominent entities, clustering of entities, and the landscape's surface in a surprisingly simple and uniform way. The effectiveness of our network visualizations is illustrated on data from the graph drawing literature.Visualization of bibliographic networks with a reshaped landscape metaphorStructure of Heterogeneous Networkshttps://puma.uni-kassel.de/bibtex/23a4a889123d20e0a4d14d06f670de54b/stummestumme2010-05-07T17:28:53+02:00graph graphs heterogenous measures multi-mode networks sna <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rumi Ghosh" itemprop="url" href="/author/Rumi%20Ghosh"><span itemprop="name">R. Ghosh</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kristina Lerman" itemprop="url" href="/author/Kristina%20Lerman"><span itemprop="name">K. Lerman</span></a></span>. </span>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)<em>cite arxiv:0906.2212.</em>Fri May 07 17:28:53 CEST 2010cite arxiv:0906.2212
Structure of Heterogeneous Networks2009graph graphs heterogenous measures multi-mode networks sna Heterogeneous networks play a key role in the evolution of communities and
the decisions individuals make. These networks link different types of
entities, for example, people and the events they attend. Network analysis
algorithms usually project such networks unto simple graphs composed of
entities of a single type. In the process, they conflate relations between
entities of different types and loose important structural information. We
develop a mathematical framework that can be used to compactly represent and
analyze heterogeneous networks that combine multiple entity and link types. We
generalize Bonacich centrality, which measures connectivity between nodes by
the number of paths between them, to heterogeneous networks and use this
measure to study network structure. Specifically, we extend the popular
modularity-maximization method for community detection to use this centrality
metric. We also rank nodes based on their connectivity to other nodes. One
advantage of this centrality metric is that it has a tunable parameter we can
use to set the length scale of interactions. By studying how rankings change
with this parameter allows us to identify important nodes in the network. We
apply the proposed method to analyze the structure of several heterogeneous
networks. We show that exploiting additional sources of evidence corresponding
to links between, as well as among, different entity types yields new insights
into network structure.
[0906.2212] Structure of Heterogeneous NetworksInformation Retrieval in Folksonomies: Search and Rankinghttps://puma.uni-kassel.de/bibtex/2087d10ce5603cef6c6a8b443700368a2/stummestumme2010-05-04T00:24:03+02:00FCA OntologyHandbook folkrank folksonomy graph information mining pagerank rank ranking retrieval search seminar2006 <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="Robert Jäschke" itemprop="url" href="/author/Robert%20J%7b%5c%22a%7dschke"><span itemprop="name">R. Jäschke</span></a></span>, <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>, 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>Seite <span itemprop="pagination">411-426</span>. </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Tue May 04 00:24:03 CEST 2010Proceedings of the 3rd European Semantic Web Conference411-426Lecture Notes in Computer ScienceInformation Retrieval in Folksonomies: Search and Ranking2006FCA OntologyHandbook folkrank folksonomy graph information mining pagerank rank ranking retrieval search seminar2006 Contextual-Logic Extension of TOSCANA.https://puma.uni-kassel.de/bibtex/2eb6e678cba7f48ae8604d59542cd79c6/stummestumme2010-04-07T13:54:41+02:002000 analysis cg cgs concept conceptual fca formal graph graphs iccs myown toscana <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="P. Eklund" itemprop="url" href="/author/P.%20Eklund"><span itemprop="name">P. Eklund</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="B. Groh" itemprop="url" href="/author/B.%20Groh"><span itemprop="name">B. Groh</span></a></span>, <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>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Wille" itemprop="url" href="/author/R.%20Wille"><span itemprop="name">R. Wille</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Conceptual Structures: Logical, Linguistic, and Computational</span>, </em></span><em>Volume 1867 von LNAI, </em><em>Seite <span itemprop="pagination">453-467</span>. </em><em>Heidelberg, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>2000<meta content="2000" itemprop="datePublished"/></span></em>)Wed Apr 07 13:54:41 CEST 2010HeidelbergConceptual Structures: Logical, Linguistic, and Computational453-467LNAIContextual-Logic Extension of TOSCANA.186720002000 analysis cg cgs concept conceptual fca formal graph graphs iccs myown toscana Publications of Gerd StummeA Geometrical Heuristic for Drawing Concept Latticeshttps://puma.uni-kassel.de/bibtex/2069db3a0aad592c82f35e1bbf701824f/stummestumme2010-04-07T13:54:41+02:001995 analysis concept drawing fca formal graph lattices myown <span class="authorEditorList"><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>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rudolf Wille" itemprop="url" href="/author/Rudolf%20Wille"><span itemprop="name">R. Wille</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Graph Drawing</span>, </em></span><em>Volume 894 von LNCS, </em><em>Seite <span itemprop="pagination">452-459</span>. </em><em>Heidelberg, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>1995<meta content="1995" itemprop="datePublished"/></span></em>)Wed Apr 07 13:54:41 CEST 2010HeidelbergGraph Drawing452-459LNCSA Geometrical Heuristic for Drawing Concept Lattices89419951995 analysis concept drawing fca formal graph lattices myown Publications of Gerd StummeGraph OLAP: Towards Online Analytical Processing on Graphshttps://puma.uni-kassel.de/bibtex/2066f245bd365c69acfff1378d72dc01e/stummestumme2009-11-12T10:32:36+01:00graph graphs olap sna <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Feida Zhu" itemprop="url" href="/author/Feida%20Zhu"><span itemprop="name">F. Zhu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Chen Chen" itemprop="url" href="/author/Chen%20Chen"><span itemprop="name">C. Chen</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Xifeng Yan" itemprop="url" href="/author/Xifeng%20Yan"><span itemprop="name">X. Yan</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jiawei Han" itemprop="url" href="/author/Jiawei%20Han"><span itemprop="name">J. Han</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Philip S Yu" itemprop="url" href="/author/Philip%20S%20Yu"><span itemprop="name">P. Yu</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008.</span>, </em></span>(<em><span>Dezember 2008<meta content="Dezember 2008" itemprop="datePublished"/></span></em>)Thu Nov 12 10:32:36 CET 2009Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008.December{Graph OLAP: Towards Online Analytical Processing on Graphs}2008graph graphs olap sna Resource: Graph OLAP: Towards Online Analytical Processing on GraphsModularity clustering is force-directed layouthttps://puma.uni-kassel.de/bibtex/20186031133dc122ffd6ff33ded32c911/stummestumme2008-07-31T11:57:32+02:00clustering communities community graph layout modularity network sna <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Noack" itemprop="url" href="/author/Andreas%20Noack"><span itemprop="name">A. Noack</span></a></span>. </span>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)Thu Jul 31 11:57:32 CEST 2008Modularity clustering is force-directed layout2008clustering communities community graph layout modularity network sna Two natural and widely used representations for the community structure of networks are clusterings, which partition the vertex set into disjoint subsets, and layouts, which assign the vertices to positions in a metric space. This paper unifies prominent characterizations of layout quality and clustering quality, by showing that energy models of pairwise attraction and repulsion subsume Newman and Girvan's modularity measure. Layouts with optimal energy are relaxations of, and are thus consistent with, clusterings with optimal modularity, which is of practical relevance because both representations are complementary and often used together.[0807.4052] Modularity clustering is force-directed layoutConceptual Graphs and Formal Concept Analysishttps://puma.uni-kassel.de/bibtex/2046f80236ce102035e0efb984ab248aa/stummestumme2007-09-05T16:00:21+02:00ag1 analysis begriffsanalyse cg concept conceptual darmstadt fba fca formal graph graphs <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rudolf Wille" itemprop="url" href="/author/Rudolf%20Wille"><span itemprop="name">R. Wille</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Conceptual Structures: Fulfilling Peirce's Dream</span>, </em></span><em>Volume 1257 von Lecture Notes in Artificial Intelligence, </em><em>Seite <span itemprop="pagination">290--303</span>. </em><em>Heidelberg, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>1997<meta content="1997" itemprop="datePublished"/></span></em>)Wed Sep 05 16:00:21 CEST 2007HeidelbergConceptual Structures: Fulfilling Peirce's Dream290--303Lecture Notes in Artificial IntelligenceConceptual Graphs and Formal Concept Analysis12571997ag1 analysis begriffsanalyse cg concept conceptual darmstadt fba fca formal graph graphs The Lattice of Concept Graphs of a Relationally Scaled Contexthttps://puma.uni-kassel.de/bibtex/27f7198720cc12f30e613b3664fafacf5/stummestumme2007-09-05T15:58:00+02:00analysis cg concept fca formal graph graphs <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Susanne Prediger" itemprop="url" href="/author/Susanne%20Prediger"><span itemprop="name">S. Prediger</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rudolf Wille" itemprop="url" href="/author/Rudolf%20Wille"><span itemprop="name">R. Wille</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">ICCS</span>, </em></span><em>Volume 1640 von Lecture Notes in Computer Science, </em><em>Seite <span itemprop="pagination">401-414</span>. </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>1999<meta content="1999" itemprop="datePublished"/></span></em>)Wed Sep 05 15:58:00 CEST 2007ICCSconf/iccs/1999401-414Lecture Notes in Computer ScienceThe Lattice of Concept Graphs of a Relationally Scaled Context16401999analysis cg concept fca formal graph graphs Content Aggregation on Knowledge Bases using Graph Clusteringhttps://puma.uni-kassel.de/bibtex/21788c88e04112a4491f19dfffb8dc39e/stummestumme2006-09-20T18:23:30+02:002006 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>)Wed Sep 20 18:23:30 CEST 2006HeidelbergThe 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.