BibSonomyburst/tag/collaborativeBibSonomy RSS feed for /tag/collaborative2012-02-16T08:36:37+01:00http://www.bibsonomy.org/bibtex/2db1001d9df13b38b26973b187983ba09/swalkswalk2012-02-14T10:40:55+01:00collaborative ontologies <span class="authorEditorList"><a href="/author/Porzel">R. Porzel</a>, and <a href="/author/Malaka">R. Malaka</a> </span>(<em>2004</em>)Tue Feb 14 10:40:55 CET 2012 Valencia, Spain Proceedings of the 16th European Conference on Artificial Intelligence A Task-based Approach for Ontology Evaluation 2004collaborative ontologies http://www.bibsonomy.org/bibtex/263e01b5caeb1803a82ec5ca401fcd41b/swalkswalk2012-02-14T09:17:51+01:00analysis collaborative networks ontologies recommender <span class="authorEditorList"><a href="/author/Brewster">C. Brewster</a>, <a href="/author/Alani">H. Alani</a>, <a href="/author/Dasmahapatra">S. Dasmahapatra</a>, and <a href="/author/Wilks">Y. Wilks</a> </span>(<em>2004</em>)Tue Feb 14 09:17:51 CET 2012 Lisbon, Portugal Proceedings of the International Conference on Language Resources and Evaluation (LREC) Data Driven Ontology Evaluation 2004analysis collaborative networks ontologies recommender http://www.bibsonomy.org/bibtex/287d6883ebd98e8810be45d7e7e4ade96/hothohotho2012-02-14T08:29:50+01:002012 bookmarking collaborative folksonomy myown recommender social tagging <span class="authorEditorList"><a href="/author/Balby Marinho">L. Balby Marinho</a>, <a href="/author/Hotho">A. Hotho</a>, <a href="/author/Jäschke">R. Jäschke</a>, <a href="/author/Nanopoulos">A. Nanopoulos</a>, <a href="/author/Rendle">S. Rendle</a>, <a href="/author/Schmidt-Thieme">L. Schmidt-Thieme</a>, <a href="/author/Stumme">G. Stumme</a>, and <a href="/author/Symeonidis">P. Symeonidis</a> </span><em>SpringerBriefs in Electrical and Computer Engineering </em><em>Springer, </em>(<em>February 2012</em>)Tue Feb 14 08:29:50 CET 2012febSpringerBriefs in Electrical and Computer EngineeringRecommender Systems for Social Tagging Systems20122012 bookmarking collaborative folksonomy myown recommender social tagging Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.http://www.bibsonomy.org/bibtex/287d6883ebd98e8810be45d7e7e4ade96/jaeschkejaeschke2012-02-13T12:52:23+01:002012 bookmarking collaborative folksonomy myown recommender social tagging <span class="authorEditorList"><a href="/author/Balby Marinho">L. Balby Marinho</a>, <a href="/author/Hotho">A. Hotho</a>, <a href="/author/Jäschke">R. Jäschke</a>, <a href="/author/Nanopoulos">A. Nanopoulos</a>, <a href="/author/Rendle">S. Rendle</a>, <a href="/author/Schmidt-Thieme">L. Schmidt-Thieme</a>, <a href="/author/Stumme">G. Stumme</a>, and <a href="/author/Symeonidis">P. Symeonidis</a> </span><em>SpringerBriefs in Electrical and Computer Engineering </em><em>Springer, </em>(<em>February 2012</em>)Mon Feb 13 12:52:23 CET 2012febSpringerBriefs in Electrical and Computer EngineeringRecommender Systems for Social Tagging Systems20122012 bookmarking collaborative folksonomy myown recommender social tagging Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/hothohotho2012-02-09T09:26:57+01:002012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Thu Feb 09 09:26:57 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/stummestumme2012-02-06T14:59:32+01:002012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Mon Feb 06 14:59:32 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/dbenzdbenz2012-02-06T14:04:20+01:002012 challenge collaborative recommendation tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Mon Feb 06 14:04:20 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 challenge collaborative recommendation tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/2b645572f4c635b2d674126f26d4303d6/sdosdo2012-02-06T14:02:41+01:00challenge collaborative recommendation system tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Mon Feb 06 14:02:41 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems322012challenge collaborative recommendation system tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/jaeschkejaeschke2012-02-06T13:47:57+01:002012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Mon Feb 06 13:47:57 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/260135f9244f783042ddd64557a59780a/swalkswalk2012-01-31T05:10:46+01:00OBI analysis biomedical collaborative investigations owl <span class="authorEditorList"><a href="/author/Courtot">Mélanie Courtot</a>, <a href="/author/Bug">William J. Bug</a>, <a href="/author/Gibson">Frank Gibson</a>, <a href="/author/Lister">Allyson L. Lister</a>, <a href="/author/Malone">James Malone</a>, <a href="/author/Schober">Daniel Schober</a>, <a href="/author/Brinkman">Ryan R. Brinkman</a>, and <a href="/author/Ruttenberg">Alan Ruttenberg</a> </span><em>OWLED, </em><em>volume 432 of CEUR Workshop Proceedings, </em><em>CEUR-WS.org, </em>(<em>2008</em>)Tue Jan 31 05:10:46 CET 2012OWLEDconf/owled/2008CEUR Workshop ProceedingsThe OWL of Biomedical Investigations.4322008OBI analysis biomedical collaborative investigations owl http://www.bibsonomy.org/bibtex/2b0e942a7c4e689cec52f6cbc7e131116/swalkswalk2012-01-28T03:13:36+01:00analysis collaborative networks semanticwiki <span class="authorEditorList"><a href="/author/Bao">Jie Bao</a>, and <a href="/author/Honavar">Vasant Honavar</a> </span><em>Proceedings of the 3rd International Workshop on Evaluation of Ontology-based Tools EON2004, </em><em>page 1--10. </em>(<em>October 2004</em>)Sat Jan 28 03:13:36 CET 2012Proceedings of the 3rd International Workshop on Evaluation of Ontology-based Tools (EON2004)oct1--10Collaborative Ontology Building with Wiki@nt - A multi-agent based ontology building environment2004analysis collaborative networks semanticwiki location = {Hiroshima, Japan}http://www.bibsonomy.org/bibtex/2eb0490ac3a182ffbf418b72fc59d873a/swalkswalk2012-01-28T03:10:22+01:00collaborative development ontology <span class="authorEditorList"><a href="/author/Sure">York Sure</a>, <a href="/author/Erdmann">Michael Erdmann</a>, <a href="/author/Angele">Jürgen Angele</a>, <a href="/author/Staab">Steffen Staab</a>, <a href="/author/Studer">Rudi Studer</a>, and <a href="/author/Wenke">Dirk Wenke</a> </span><em>International Semantic Web Conference, </em><em>volume 2342 of Lecture Notes in Computer Science, </em><em>page 221-235. </em><em>Springer, </em>(<em>2002</em>)Sat Jan 28 03:10:22 CET 2012International Semantic Web Conferenceconf/semweb/2002221-235Lecture Notes in Computer ScienceOntoEdit: Collaborative Ontology Development for the Semantic Web.23422002collaborative development ontology http://www.bibsonomy.org/bibtex/2c788cb488150a739f83f6f6c571ead31/swalkswalk2012-01-28T02:21:18+01:00analysis collaborative networks ontologyengineering <span class="authorEditorList"><a href="/author/Simperl">Elena Simperl</a>, and <a href="/author/Luczak-Rösch">Markus Luczak-Rösch</a> </span><em>to appear</em> (<em>2011</em>)Sat Jan 28 02:21:18 CET 2012(to appear)Collaborative Ontology Engineering: A Survey2011analysis collaborative networks ontologyengineering http://www.bibsonomy.org/bibtex/28516d94c1f7aa1e391ddd3ace4caa23b/swalkswalk2012-01-26T20:47:16+01:00analysis collaborative information introduction retrieval <span class="authorEditorList"><a href="/author/Manning">Christopher D. Manning</a>, <a href="/author/Raghavan">Prabhakar Raghavan</a>, and <a href="/author/Schütze">Hinrich Schütze</a> </span><em>Cambridge University Press, </em><em>Cambridge, </em>(<em>July 2008</em>)Thu Jan 26 20:47:16 CET 2012CambridgeJuliIntroduction to Information Retrieval2008analysis collaborative information introduction retrieval http://www.bibsonomy.org/bibtex/2fb2819f0951eb5c9e5ba09fd2c306443/swalkswalk2012-01-26T05:37:59+01:00analysis collaborative networks ontologies <span class="authorEditorList"><a href="/author/Breese">John S. Breese</a>, <a href="/author/Heckerman">David Heckerman</a>, and <a href="/author/Kadie">Carl Myers Kadie</a> </span><em>UAI, </em><em>page 43-52. </em><em>Morgan Kaufmann, </em>(<em>1998</em>)Thu Jan 26 05:37:59 CET 2012UAIconf/uai/199843-52Empirical Analysis of Predictive Algorithms for Collaborative Filtering.1998analysis collaborative networks ontologies http://www.bibsonomy.org/bibtex/2f3025640a7386381a290f88747e269b9/swalkswalk2012-01-24T23:07:44+01:00analysis collaborative networks ontologies <span class="authorEditorList"><a href="/author/Goldberg">David Goldberg</a>, <a href="/author/Nichols">David A. Nichols</a>, <a href="/author/Oki">Brian M. Oki</a>, and <a href="/author/Terry">Douglas B. Terry</a> </span><em>Commun. ACM</em> <em>35(12):61-70</em> (<em>1992</em>)Tue Jan 24 23:07:44 CET 2012Commun. ACM1261-70Using Collaborative Filtering to Weave an Information Tapestry.351992analysis collaborative networks ontologies http://www.bibsonomy.org/bibtex/29965d75ca32a92b5be425d54d3ed5ef6/swalkswalk2012-01-24T20:00:55+01:00analysis collaborative filtering survey <span class="authorEditorList"><a href="/author/Su">Xiaoyuan Su</a>, and <a href="/author/Khoshgoftaar">Taghi M. Khoshgoftaar</a> </span><em>Adv. in Artif. Intell.</em> (<em>January 2009</em>)Tue Jan 24 20:00:55 CET 2012New York, NY, United StatesAdv. in Artif. Intell.January4:2--4:2A survey of collaborative filtering techniques20092009analysis collaborative filtering survey http://www.bibsonomy.org/bibtex/22be1c0d5283d3e0bd998b94a5faee110/swalkswalk2012-01-23T09:45:09+01:00collaborative filtering grouplens <span class="authorEditorList"><a href="/author/Resnick">Paul Resnick</a>, <a href="/author/Iacovou">Neophytos Iacovou</a>, <a href="/author/Suchak">Mitesh Suchak</a>, <a href="/author/Bergstrom">Peter Bergstrom</a>, and <a href="/author/Riedl">John Riedl</a> </span><em>Proceedings of the 1994 ACM Conference on Computer supported cooperative work, </em><em>page 175--186. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>1994</em>)Mon Jan 23 09:45:09 CET 2012New York, NY, USAProceedings of the 1994 ACM Conference on Computer supported cooperative work175--186GroupLens: an open architecture for collaborative filtering of netnews1994collaborative filtering grouplens http://www.bibsonomy.org/bibtex/238f009374ff82c99e73160e376b05b55/swalkswalk2012-01-23T09:23:23+01:00analysis collaborative measuring wikipedia <span class="authorEditorList"><a href="/author/Voss">Jakob Voss</a> </span><em>Proceedings International Conference of the International Society for Scientometrics and Informetrics: 10 th, </em>(<em>2005</em>)Mon Jan 23 09:23:23 CET 2012Proceedings International Conference of the International Society for Scientometrics and Informetrics: 10 thMeasuring Wikipedia2005analysis collaborative measuring wikipedia http://www.bibsonomy.org/bibtex/2090b38bd9dc4af791b5e88a00d871fcf/swalkswalk2012-01-23T09:20:45+01:00analysis collaborative evaluation wikipedia <span class="authorEditorList"><a href="/author/Lih">Andrew Lih</a> </span><em>In Proceedings of the 5th International Symposium on Online Journalism, </em><em>page 16--17. </em>(<em>2004</em>)Mon Jan 23 09:20:45 CET 2012In Proceedings of the 5th International Symposium on Online Journalism16--17Wikipedia as Participatory journalism: reliable sources? metrics for evaluating collaborative media as a news resource2004analysis collaborative evaluation wikipedia Wikipedia is an Internet-based, user contributed encyclopedia that is collaboratively edited, and utilizes the wiki concept – the idea that any user on the Internet can change any page within the Web site, even anonymously. Paradoxically, this seemingly chaotic process has created a highly regarded reference on the Internet. Wikipedia has emerged as the largest example of participatory journalism to date – facilitating many-to-many communication among users editing articles, all working towards maintaining a neutral point of view — Wikipedia’s mantra. This study examines the growth of Wikipedia and analyzes the crucial technologies and community policies that have enabled the project to prosper. It also analyzes Wikipedia’s articles that have been cited in the news media, and establishes a set of metrics based on established encyclopedia taxonomies and analyzes the trends in Wikipedia being used as a source.