PUMA publications for /user/benz/pagerankhttps://puma.uni-kassel.de/user/benz/pagerankPUMA RSS feed for /user/benz/pagerank2024-03-29T02:41:51+01:00The Anatomy of a Large-Scale Hypertextual Web Search Enginehttps://puma.uni-kassel.de/bibtex/2fc936cec60b1b7ab69f230f14139e8ab/benzbenz2011-08-08T08:41:22+02:00pagerank <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sergey Brin" itemprop="url" href="/author/Sergey%20Brin"><span itemprop="name">S. Brin</span></a></span>, and <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lawrence Page" itemprop="url" href="/author/Lawrence%20Page"><span itemprop="name">L. Page</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>Computer Networks and ISDN Systems</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">30 </span></span>(<span itemprop="issueNumber">1-7</span>):
<span itemprop="pagination">107--117</span></em> </span>(<em><span>April 1998<meta content="April 1998" itemprop="datePublished"/></span></em>)Mon Aug 08 08:41:22 CEST 2011Computer Networks and ISDN SystemsApril1-7107--117{T}he {A}natomy of a {L}arge-{S}cale {H}ypertextual {W}eb {S}earch {E}ngine301998pagerank Index design and query processing for graph conductance searchhttps://puma.uni-kassel.de/bibtex/2dcc951cd461fe1c454db7a738429d421/benzbenz2011-02-04T16:10:21+01:00index conductance pagerank interactive <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Soumen Chakrabarti" itemprop="url" href="/author/Soumen%20Chakrabarti"><span itemprop="name">S. Chakrabarti</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Amit Pathak" itemprop="url" href="/author/Amit%20Pathak"><span itemprop="name">A. Pathak</span></a></span>, and <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Manish Gupta" itemprop="url" href="/author/Manish%20Gupta"><span itemprop="name">M. Gupta</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>The VLDB Journal</em></span></span> </span>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)Fri Feb 04 16:10:21 CET 2011Berlin / HeidelbergThe VLDB Journal1-26Index design and query processing for graph conductance search2010index conductance pagerank interactive Graph conductance queries, also known as personalized PageRank and related to random walks with restarts, were originally proposed to assign a hyperlink-based prestige score to Web pages. More general forms of such queries are also very useful for ranking in entity-relation (ER) graphs used to represent relational, XML and hypertext data. Evaluation of PageRank usually involves a global eigen computation. If the graph is even moderately large, interactive response times may not be possible. Recently, the need for interactive PageRank evaluation has increased. The graph may be fully known only when the query is submitted. Browsing actions of the user may change some inputs to the PageRank computation dynamically. In this paper, we describe a system that analyzes query workloads and the ER graph, invests in limited offline indexing, and exploits those indices to achieve essentially constant-time query processing, even as the graph size scales. Our techniques—data and query statistics collection, index selection and materialization, and query-time index exploitation—have parallels in the extensive relational query optimization literature, but is applied to supporting novel graph data repositories. We report on experiments with five temporal snapshots of the CiteSeer ER graph having 74–702 thousand entity nodes, 0.17–1.16 million word nodes, 0.29–3.26 million edges between entities, and 3.29–32.8 million edges between words and entities. We also used two million actual queries from CiteSeer’s logs. Queries run 3–4 orders of magnitude faster than whole-graph PageRank, the gap growing with graph size. Index size is smaller than a text index. Ranking accuracy is 94–98% with reference to whole-graph PageRank.Trend Detection in Folksonomieshttps://puma.uni-kassel.de/bibtex/242cda5911e901eadd0ac6a106a6aa1dc/benzbenz2011-02-04T16:10:09+01:00intranet 2006 trend pagerank hotho schmitz jaeschke l3s itegpub detection triadic stumme nepomuk folksonomy tagorapub folkrank UniK <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%c3%a4schke"><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>, and <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">Proc. First International Conference on Semantics And Digital Media Technology (SAMT)</span>, </em></span><em>volume 4306 of LNCS, </em><em>page <span itemprop="pagination">56-70</span>. </em><em>Heidelberg, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>December 2006<meta content="December 2006" itemprop="datePublished"/></span></em>)Fri Feb 04 16:10:09 CET 2011HeidelbergProc. First International Conference on Semantics And Digital Media Technology (SAMT) December56-70LNCSTrend Detection in Folksonomies43062006intranet 2006 trend pagerank hotho schmitz jaeschke l3s itegpub detection triadic stumme nepomuk folksonomy tagorapub folkrank UniK As the number of resources on the web exceeds by far the number ofdocuments one can track, it becomes increasingly difficult to remainup to date on ones own areas of interest. The problem becomes moresevere with the increasing fraction of multimedia data, from whichit is difficult to extract some conceptual description of theircontents.One way to overcome this problem are social bookmark tools, whichare rapidly emerging on the web. In such systems, users are settingup lightweight conceptual structures called folksonomies, andovercome thus the knowledge acquisition bottleneck. As more and morepeople participate in the effort, the use of a common vocabularybecomes more and more stable. We present an approach for discoveringtopic-specific trends within folksonomies. It is based on adifferential adaptation of the PageRank algorithm to the triadichypergraph structure of a folksonomy. The approach allows for anykind of data, as it does not rely on the internal structure of thedocuments. In particular, this allows to consider different datatypes in the same analysis step. We run experiments on a large-scalereal-world snapshot of a social bookmarking system.Das Entstehen von Semantik in BibSonomyhttps://puma.uni-kassel.de/bibtex/2a333df6fdc7ff9322e3ce03988a7965e/benzbenz2011-02-04T16:09:42+01:00tags semantik 2006 pagerank tagging association hotho bibsonomy schmitz jaeschke rules semantics stumme BibSonomy nepomuk folksonomy tagorapub folksonomies tagora folkrank UniK ol_web2.0 emergentsemantics_evidence <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%c3%a4schke"><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>, and <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">Social Software in der Wertschöpfung</span>, </em></span><em>Baden-Baden, </em><em><span itemprop="publisher">Nomos</span>, </em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Fri Feb 04 16:09:42 CET 2011Baden-BadenSocial Software in der WertschöpfungDas Entstehen von Semantik in BibSonomy2006tags semantik 2006 pagerank tagging association hotho bibsonomy schmitz jaeschke rules semantics stumme BibSonomy nepomuk folksonomy tagorapub folksonomies tagora folkrank UniK ol_web2.0 emergentsemantics_evidence Immer mehr Soziale-Lesezeichen-Systeme entstehen im heutigen Web. In solchen Systemen erstellen die Nutzer leichtgewichtige begriffliche Strukturen, so genannte Folksonomies. Ihren Erfolg verdanken sie der Tatsache, dass man keine speziellen Fähigkeiten benötigt, um an der Gestaltung mitzuwirken. In diesem Artikel beschreiben wir unser System BibSonomy. Es erlaubt das Speichern, Verwalten und Austauschen sowohl von Lesezeichen (Bookmarks) als auch von Literaturreferenzen in Form von BibTeX-Einträgen. Die Entwicklung des verwendeten Vokabulars und der damit einhergehenden Entstehung einer gemeinsamen Semantik wird detailliert diskutiert.Information Retrieval in Folksonomies: Search and Rankinghttps://puma.uni-kassel.de/bibtex/23c301945817681d637ee43901c016939/benzbenz2011-02-04T16:09:30+01:002006 folkrank folksonomy graph iccs_example information l3s mining ol_web2.0 pagerank rank ranking retrieval search seminar2006 testttag trias_example webzu widely_related <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%c3%a4schke"><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>, and <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 of Lecture Notes in Computer Science, </em><em>page <span itemprop="pagination">411-426</span>. </em><em>Heidelberg, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>June 2006<meta content="June 2006" itemprop="datePublished"/></span></em>)Fri Feb 04 16:09:30 CET 2011HeidelbergThe Semantic Web: Research and ApplicationsJune411-426Lecture Notes in Computer ScienceInformation Retrieval in Folksonomies: Search and Ranking401120062006 folkrank folksonomy graph iccs_example information l3s mining ol_web2.0 pagerank rank ranking retrieval search seminar2006 testttag trias_example webzu widely_related Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies,called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to findcommunities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.