BibSonomyburst/author/RobuBibSonomy RSS feed for /author/Robu2012-02-15T10:59:35+01:00http://www.bibsonomy.org/bibtex/22fcd83a2e7103a3546219c6f7f078cdb/peter.b825peter.b8252012-02-11T19:24:16+01:00imported <span class="authorEditorList"><a href="/author/Halpin">Harry Halpin</a>, <a href="/author/Robu">Valentin Robu</a>, and <a href="/author/Shepherd">Hana Shepherd</a> </span><em>Proceedings of the 16th international conference on World Wide Web, </em><em>page 211--220. </em><em>New York and NY and USA, </em><em>ACM, </em>(<em>2007</em>)Sat Feb 11 19:24:16 CET 2012New York and NY and USAProceedings of the 16th international conference on World Wide Web211--220WWW '07The complex dynamics of collaborative tagging2007imported http://www.bibsonomy.org/bibtex/29de71be90e177d68705132b3e16bf0f1/jcsjcs2012-01-31T21:08:40+01:00negotiation <span class="authorEditorList"><a href="/author/Robu">Valentin Robu</a>, <a href="/author/Somefun">D. J. A. Somefun</a>, and <a href="/author/La Poutré">J. A. La Poutré</a> </span><em>AAMAS &#039;05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, </em><em>page 280--287. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2005</em>)Tue Jan 31 21:08:40 CET 2012New York, NY, USAAAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems280--287{Modeling complex multi-issue negotiations using utility graphs}2005negotiation {This paper presents an agent strategy for complex bilateral negotiations over many issues with inter-dependent valuations. We use ideas inspired by graph theory and probabilistic influence networks to derive efficient heuristics for negotiations about multiple issues. Experimental results show --- under relatively weak assumptions with respect to the structure of the utility functions - that the developed approach leads to Pareto-efficient outcomes. Moreover, Pareto-efficiency can be reached with few negotiation steps, because we explicitly model and utilize the underlying graphical structure of complex utility functions. Consequently, our approach is applicable to domains where reaching an efficient outcome in a limited amount of time is important. Furthermore, unlike other solutions for high-dimensional negotiations, the proposed approach does not require a mediator.}http://www.bibsonomy.org/bibtex/22fcd83a2e7103a3546219c6f7f078cdb/chriskoernerchriskoerner2012-01-23T11:12:32+01:00dynamics tagging taggingsurvey <span class="authorEditorList"><a href="/author/Halpin">Harry Halpin</a>, <a href="/author/Robu">Valentin Robu</a>, and <a href="/author/Shepherd">Hana Shepherd</a> </span><em>Proceedings of the 16th international conference on World Wide Web, </em><em>page 211--220. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Mon Jan 23 11:12:32 CET 2012New York, NY, USAProceedings of the 16th international conference on World Wide Web211--220WWW '07The complex dynamics of collaborative tagging2007dynamics tagging taggingsurvey The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.The complex dynamics of collaborative tagginghttp://www.bibsonomy.org/bibtex/2bcd93a460d0a0a6653c4adf7585deda5/chriskoernerchriskoerner2012-01-23T09:21:07+01:00taggingsurvey tags voc <span class="authorEditorList"><a href="/author/Robu">Valentin Robu</a>, <a href="/author/Halpin">Harry Halpin</a>, and <a href="/author/Shepherd">Hana Shepherd</a> </span><em>ACM Trans. Web</em> (<em>September 2009</em>)Mon Jan 23 09:21:07 CET 2012New York, NY, USAACM Trans. WebSeptember14:1--14:34Emergence of consensus and shared vocabularies in collaborative tagging systems32009taggingsurvey tags voc This article uses data from the social bookmarking site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users.</p> <p>First, we study the formation of stable distributions in tagging systems, seen as an implicit form of &ldquo;consensus&rdquo; reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed for both the most utilized tags at the top of tag distributions and the so-called long tail.</p> <p>Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine.</p> <p>Although the empirical analysis presented in this article is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across other websites that employ tagging.Emergence of consensus and shared vocabularies in collaborative tagging systemshttp://www.bibsonomy.org/bibtex/22fcd83a2e7103a3546219c6f7f078cdb/beatebeate2011-11-28T07:27:05+01:00folksonomy model tagging <span class="authorEditorList"><a href="/author/Halpin">Harry Halpin</a>, <a href="/author/Robu">Valentin Robu</a>, and <a href="/author/Shepherd">Hana Shepherd</a> </span><em>Proceedings of the 16th international conference on World Wide Web, </em><em>page 211--220. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Mon Nov 28 07:27:05 CET 2011New York, NY, USAProceedings of the 16th international conference on World Wide Web211--220WWW '07The complex dynamics of collaborative tagging2007folksonomy model tagging {The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.}http://www.bibsonomy.org/bibtex/22fcd83a2e7103a3546219c6f7f078cdb/victorinostarvictorinostar2011-11-25T13:04:27+01:00ambiguity collaborative distribution dynamics generative_model power_law preferential_attachment stability synonymy tagging tripartite <span class="authorEditorList"><a href="/author/Halpin">Harry Halpin</a>, <a href="/author/Robu">Valentin Robu</a>, and <a href="/author/Shepherd">Hana Shepherd</a> </span><em>Proceedings of the 16th international conference on World Wide Web, </em><em>page 211--220. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Fri Nov 25 13:04:27 CET 2011New York, NY, USAProceedings of the 16th international conference on World Wide Web211--220WWW '07{The complex dynamics of collaborative tagging}2007ambiguity collaborative distribution dynamics generative_model power_law preferential_attachment stability synonymy tagging tripartite {The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.}http://www.bibsonomy.org/bibtex/2670bd71da5a43942237443be0c499c0c/dblpdblp2011-11-18T00:00:00+01:00dblp <span class="authorEditorList"><a href="/author/Gerding">Enrico H. Gerding</a>, <a href="/author/Robu">Valentin Robu</a>, <a href="/author/Stein">Sebastian Stein</a>, <a href="/author/Parkes">David C. Parkes</a>, <a href="/author/Rogers">Alex Rogers</a>, and <a href="/author/Jennings">Nicholas R. Jennings</a> </span><em>AAMAS, </em><em>page 811-818. </em><em>IFAAMAS, </em>(<em>2011</em>)Fri Nov 18 00:00:00 CET 2011AAMASconf/atal/2011811-818Online mechanism design for electric vehicle charging.2011dblp http://www.bibsonomy.org/bibtex/202e61bb220ecd52081df8dae46776879/dblpdblp2011-11-18T00:00:00+01:00dblp <span class="authorEditorList"><a href="/author/Chalkiadakis">Georgios Chalkiadakis</a>, <a href="/author/Robu">Valentin Robu</a>, <a href="/author/Kota">Ramachandra Kota</a>, <a href="/author/Rogers">Alex Rogers</a>, and <a href="/author/Jennings">Nicholas R. Jennings</a> </span><em>AAMAS, </em><em>page 787-794. </em><em>IFAAMAS, </em>(<em>2011</em>)Fri Nov 18 00:00:00 CET 2011AAMASconf/atal/2011787-794Cooperatives of distributed energy resources for efficient virtual power plants.2011dblp http://www.bibsonomy.org/bibtex/22fcd83a2e7103a3546219c6f7f078cdb/dimitargndimitargn2011-09-28T23:59:32+02:00network-analysis qual tagging <span class="authorEditorList"><a href="/author/Halpin">Harry Halpin</a>, <a href="/author/Robu">Valentin Robu</a>, and <a href="/author/Shepherd">Hana Shepherd</a> </span><em>Proceedings of the 16th international conference on World Wide Web, </em><em>page 211--220. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Wed Sep 28 23:59:32 CEST 2011New York, NY, USAProceedings of the 16th international conference on World Wide Web211--220WWW '07{The complex dynamics of collaborative tagging}2007network-analysis qual tagging {The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.}http://www.bibsonomy.org/bibtex/2dbfe955c1ac8d0c557e5215bff817d4f/dimitargndimitargn2011-09-28T23:59:32+02:00network-analysis qual tagging <span class="authorEditorList"><a href="/author/Robu">Valentin Robu</a>, <a href="/author/Halpin">Harry Halpin</a>, and <a href="/author/Shepherd">Hana Shepherd</a> </span><em>ACM Trans. Web</em> <em>3(4):1--34</em> (<em>September 2009</em>)Wed Sep 28 23:59:32 CEST 2011New York, NY, USAACM Trans. Websep41--34{Emergence of consensus and shared vocabularies in collaborative tagging systems}32009network-analysis qual tagging {This article uses data from the social bookmarking site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of ” consensus” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed for both the most utilized tags at the top of tag distributions and the so-called long tail. Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine. Although the empirical analysis presented in this article is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across other websites that employ tagging.}http://www.bibsonomy.org/bibtex/22c2c689cb9946670785d0940e9dab324/folkefolke2011-08-26T22:12:37+02:00analysis collaborative dynamics history network power-law <span class="authorEditorList"><a href="/author/Halpin">Harry Halpin</a>, <a href="/author/Robu">Valentin Robu</a>, and <a href="/author/Shepherd">Hana Shepherd</a> </span><em>WWW &#039;07: Proceedings of the 16th international conference on World Wide Web, </em><em>page 211--220. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Fri Aug 26 22:12:37 CEST 2011New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web211--220The complex dynamics of collaborative tagging2007analysis collaborative dynamics history network power-law The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site del.icio.us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.The complex dynamics of collaborative tagginghttp://www.bibsonomy.org/bibtex/22c2c689cb9946670785d0940e9dab324/vipirttivipirtti2011-08-26T16:22:54+02:00stability tagging <span class="authorEditorList"><a href="/author/Halpin">Harry Halpin</a>, <a href="/author/Robu">Valentin Robu</a>, and <a href="/author/Shepherd">Hana Shepherd</a> </span><em>WWW &#039;07: Proceedings of the 16th international conference on World Wide Web, </em><em>page 211--220. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Fri Aug 26 16:22:54 CEST 2011New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web211--220The complex dynamics of collaborative tagging2007stability tagging The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.http://www.bibsonomy.org/bibtex/2ed412e421d834d7296d637e6b413dec8/dblpdblp2011-08-10T00:00:00+02:00dblp <span class="authorEditorList"><a href="/author/Ida">Tetsuo Ida</a>, <a href="/author/Tepeneu">Dorin Tepeneu</a>, <a href="/author/Buchberger">Bruno Buchberger</a>, and <a href="/author/Robu">Judit Robu</a> </span><em>AISC, </em><em>volume 3249 of Lecture Notes in Computer Science, </em><em>page 132-142. </em><em>Springer, </em>(<em>2004</em>)Wed Aug 10 00:00:00 CEST 2011AISCconf/aisc/2004132-142Lecture Notes in Computer ScienceProving and Constraint Solving in Computational Origami.32492004dblp http://www.bibsonomy.org/bibtex/2bd898d02e47fe807477c0ae8b01ec64e/dblpdblp2011-08-09T00:00:00+02:00dblp <span class="authorEditorList"><a href="/author/Williams">Colin R. Williams</a>, <a href="/author/Robu">Valentin Robu</a>, <a href="/author/Gerding">Enrico H. Gerding</a>, and <a href="/author/Jennings">Nicholas R. Jennings</a> </span><em>IJCAI, </em><em>page 432-438. </em><em>IJCAI/AAAI, </em>(<em>2011</em>)Tue Aug 09 00:00:00 CEST 2011IJCAIconf/ijcai/2011432-438Using Gaussian Processes to Optimise Concession in Complex Negotiations against Unknown Opponents.2011dblp http://www.bibsonomy.org/bibtex/22c2c689cb9946670785d0940e9dab324/veronicasuveronicasu2011-07-29T13:34:19+02:00folksonomy tagging <span class="authorEditorList"><a href="/author/Halpin">Harry Halpin</a>, <a href="/author/Robu">Valentin Robu</a>, and <a href="/author/Shepherd">Hana Shepherd</a> </span><em>WWW &#039;07: Proceedings of the 16th international conference on World Wide Web, </em><em>page 211--220. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Fri Jul 29 13:34:19 CEST 2011New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web211--220The complex dynamics of collaborative tagging2007folksonomy tagging The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.http://www.bibsonomy.org/bibtex/22c2c689cb9946670785d0940e9dab324/beatebeate2011-07-20T09:38:28+02:00collaboration dynamics social-tagging tagging <span class="authorEditorList"><a href="/author/Halpin">Harry Halpin</a>, <a href="/author/Robu">Valentin Robu</a>, and <a href="/author/Shepherd">Hana Shepherd</a> </span><em>WWW &#039;07: Proceedings of the 16th international conference on World Wide Web, </em><em>page 211--220. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Wed Jul 20 09:38:28 CEST 2011New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web211--220The complex dynamics of collaborative tagging2007collaboration dynamics social-tagging tagging The complex dynamics of collaborative tagginghttp://www.bibsonomy.org/bibtex/28b5d6710b0a450827bb4776a7b85aad2/dblpdblp2011-06-16T00:00:00+02:00dblp <span class="authorEditorList"><a href="/author/Robu">Valentin Robu</a>, and <a href="/author/Poutré">Han La Poutré</a> </span><em>PRIMA, </em><em>volume 4078 of Lecture Notes in Computer Science, </em><em>page 192-206. </em><em>Springer, </em>(<em>2005</em>)Thu Jun 16 00:00:00 CEST 2011PRIMAconf/prima/2005192-206Lecture Notes in Computer ScienceLearning the Structure of Utility Graphs Used in Multi-issue Negotiation through Collaborative Filtering - Preliminary Version.40782005dblp http://www.bibsonomy.org/bibtex/22c2c689cb9946670785d0940e9dab324/lichtsprunglichtsprung2011-05-19T14:31:37+02:00collaborative folksonomy <span class="authorEditorList"><a href="/author/Halpin">Harry Halpin</a>, <a href="/author/Robu">Valentin Robu</a>, and <a href="/author/Shepherd">Hana Shepherd</a> </span><em>WWW &#039;07: Proceedings of the 16th international conference on World Wide Web, </em><em>page 211--220. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Thu May 19 14:31:37 CEST 2011New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web211--220The complex dynamics of collaborative tagging2007collaborative folksonomy The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.http://www.bibsonomy.org/bibtex/2266b31ad3599499aacf593e82e775c5b/vipirttivipirtti2011-02-25T14:49:55+01:00collaborative semantics tagging <span class="authorEditorList"><a href="/author/Halpin">Harry Halpin</a>, <a href="/author/Robu">Valentin Robu</a>, and <a href="/author/Shepard">Hana Shepard</a> </span><em>Proceedings of the 1st Semantic Authoring and Annotation Workshop SAAW&#039;06, </em>(<em>2006</em>)Fri Feb 25 14:49:55 CET 2011Proceedings of the 1st Semantic Authoring and Annotation Workshop (SAAW'06)The Dynamics and Semantics of Collaborative Tagging2006collaborative semantics tagging The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including the dynamics of such systems and whether coherent classification schemes can emerge from undirected tagging by users. Currently millions of users are using collaborative tagging without centrally organizing principles, and many suspect this exhibits features considered to be indicative of a complex system. If this is the case, it remains to be seem whether collaborative tagging by users over time leads to emergent classi- fication schemes that could be formalized into an ontology usable by the Semantic Web. This paper uses data from �popular� tagged sites on the social bookmarking site del.icio.us to examine the dynamics of such collaborative tagging systems. In particular, we are trying to determine whether the distribution of tag frequencies stabilizes, which indicates a degree of cohesion or consensus among users about the optimal tags to describe particular sites. We use tag co-occurrence networks for a sample domain of tags to analyze the meaning of particular tags given their relationship to other tags and automatically create an ontology. We also produce a generative model of collaborative tagging in order to model and understand some of the basic dynamics behind the process.http://www.bibsonomy.org/bibtex/271cf71bd43f64d17fb284177219c1b32/dblpdblp2011-02-23T00:00:00+01:00dblp <span class="authorEditorList"><a href="/author/Robu">Bogdan Robu</a>, <a href="/author/Budinger">Valerie Budinger</a>, <a href="/author/Baudouin">Lucie Baudouin</a>, <a href="/author/Prieur">Christophe Prieur</a>, and <a href="/author/Arzelier">Denis Arzelier</a> </span><em>CDC, </em><em>page 3146-3151. </em><em>IEEE, </em>(<em>2010</em>)Wed Feb 23 00:00:00 CET 2011CDCconf/cdc/20103146-3151Simultaneous H∞ vibration control of fluid/plate system via reduced-order controller.2010dblp