PUMA publications for /user/hotho/WWWhttps://puma.uni-kassel.de/user/hotho/WWWPUMA RSS feed for /user/hotho/WWW2024-03-19T05:21:20+01:00How Social is Social Tagging?https://puma.uni-kassel.de/bibtex/211fab5468dd4b4e3db662ea5e68df8e0/hothohotho2014-10-29T11:23:34+01:002014 WWW analyis behavior log myown 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>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Daniel Zoller" itemprop="url" href="/author/Daniel%20Zoller"><span itemprop="name">D. Zoller</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Philipp Singer" itemprop="url" href="/author/Philipp%20Singer"><span itemprop="name">P. Singer</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thomas Niebler" itemprop="url" href="/author/Thomas%20Niebler"><span itemprop="name">T. Niebler</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="Markus Strohmaier" itemprop="url" href="/author/Markus%20Strohmaier"><span itemprop="name">M. Strohmaier</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 23rd International World Wide Web Conference</span>, </em></span><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2014<meta content="2014" itemprop="datePublished"/></span></em>)Wed Oct 29 11:23:34 CET 2014New York, NY, USAProceedings of the 23rd International World Wide Web ConferenceWWW 2014How Social is Social Tagging?20142014 WWW analyis behavior log myown social tagging Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosityhttps://puma.uni-kassel.de/bibtex/245f8d8f2a8251a5e988c596a5ebb3f2d/hothohotho2010-06-17T20:34:50+02:002010 collaborative myown tagging taggingsurvey www www2010 <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christian Körner" itemprop="url" href="/author/Christian%20K%c3%b6rner"><span itemprop="name">C. Körner</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dominik Benz" itemprop="url" href="/author/Dominik%20Benz"><span itemprop="name">D. Benz</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Markus Strohmaier" itemprop="url" href="/author/Markus%20Strohmaier"><span itemprop="name">M. Strohmaier</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="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 19th International World Wide Web Conference (WWW 2010)</span>, </em></span><em>Raleigh, NC, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>April 2010<meta content="April 2010" itemprop="datePublished"/></span></em>)Thu Jun 17 20:34:50 CEST 2010Raleigh, NC, USAProceedings of the 19th International World Wide Web Conference (WWW 2010)aprStop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity20102010 collaborative myown tagging taggingsurvey www www2010 Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.Extraction and Classification of Dense Communities in the WebAuthorshttps://puma.uni-kassel.de/bibtex/2480a63c3e6847dc8a9ebd3de040501db/hothohotho2007-05-10T00:12:21+02:002007 clustering graph www <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yon Dourisboure" itemprop="url" href="/author/Yon%20Dourisboure"><span itemprop="name">Y. Dourisboure</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Filippo Geraci" itemprop="url" href="/author/Filippo%20Geraci"><span itemprop="name">F. Geraci</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marco Pellegrini" itemprop="url" href="/author/Marco%20Pellegrini"><span itemprop="name">M. Pellegrini</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc of the wwww</span>, </em></span>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Thu May 10 00:12:21 CEST 2007Proc of the wwwwExtraction and Classification of Dense Communities in the WebAuthors20072007 clustering graph www The World Wide Web (WWW) is rapidly becoming important for society as a medium for sharing data, information and services, and there is a growing interest in tools for understanding collective behaviors and emerging phenomena in the WWW. In this paper we focus on the problem of searching and classifying {\em communities} in the web. Loosely speaking a community is a group of pages related to a common interest. More formally communities have been associated in the computer science literature with the existence of a locally dense sub-graph of the web-graph (where web pages are nodes and hyper-links are arcs of the web-graph). The core of our contribution is a new scalable algorithm for finding relatively dense subgraphs in massive graphs. We apply our algorithm on web-graphs built on three publicly available large crawls of the web (with raw sizes up to 120M nodes and 1G arcs). The effectiveness of our algorithm in finding dense subgraphs is demonstrated experimentally by embedding artificial communities in the web-graph and counting how many of these are blindly found. Effectiveness increases with the size and density of the communities: it is close to 100\% for communities of a thirty nodes or more (even at low density). It is still about 80\% even for communities of twenty nodes with density over $50\%$ of the arcs present. At the lower extremes the algorithm catches 35\% of dense communities made of ten nodes. We complete our Community Watch system by clustering the communities found in the web-graph into homogeneous groups by topic and labelling each group by representative keywords.WWW2007 Paper DetailsFinding related pages in the World Wide Webhttps://puma.uni-kassel.de/bibtex/27d3c70d55c118425216a7375f749c2f2/hothohotho2006-01-10T13:55:26+01:00www search page find <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J. Dean" itemprop="url" href="/author/J.%20Dean"><span itemprop="name">J. Dean</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M.R. Henzinger" itemprop="url" href="/author/M.R.%20Henzinger"><span itemprop="name">M. Henzinger</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the Eighth International World Wide Web Conference WWW-1999</span>, </em></span><em>Toronto, </em>(<em><span>Mai 1999<meta content="Mai 1999" itemprop="datePublished"/></span></em>)Tue Jan 10 13:55:26 CET 2006TorontoProceedings of the Eighth International World Wide Web Conference WWW-1999MayFinding related pages in the World Wide Web1999www search page find Data preparation for mining world wide web browsing patternshttps://puma.uni-kassel.de/bibtex/2e515dc2a8adbc7fa84b7fe968b61391e/hothohotho2006-01-08T11:47:30+01:00www preparation data browsing pattern mining <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Cooley" itemprop="url" href="/author/R.%20Cooley"><span itemprop="name">R. Cooley</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="B. Mobasher" itemprop="url" href="/author/B.%20Mobasher"><span itemprop="name">B. Mobasher</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J. Srivastava" itemprop="url" href="/author/J.%20Srivastava"><span itemprop="name">J. Srivastava</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>Journal of Knowledge and Information Systems</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">1 </span></span>(<span itemprop="issueNumber">1</span>):
<span itemprop="pagination">5--32</span></em> </span>(<em><span>1999<meta content="1999" itemprop="datePublished"/></span></em>)Sun Jan 08 11:47:30 CET 2006Journal of Knowledge and Information Systems15--32Data preparation for mining world wide web browsing patterns11999www preparation data browsing pattern mining