BibSonomyburst/tag/folksonomyBibSonomy RSS feed for /tag/folksonomy2012-02-15T18:21:48+01:00http://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/20834c85062338a7da4a014923467c2ca/folkefolke2012-02-06T15:57:57+01:00analysis entropy folksonomy <span class="authorEditorList"><a href="/author/Eda">Takeharu Eda</a>, <a href="/author/Yoshikawa">Masatoshi Yoshikawa</a>, <a href="/author/Uchiyama">Toshio Uchiyama</a>, and <a href="/author/Uchiyama">Tadasu Uchiyama</a> </span><em>World Wide Web</em> (<em>December 2009</em>)Mon Feb 06 15:57:57 CET 2012Hingham, MA, USAWorld Wide WebDecember421--440The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags122009analysis entropy folksonomy In this paper, we evaluate the effectiveness of a semantic smoothing technique to organize folksonomy tags. Folksonomy tags have no explicit relations and vary because they form uncontrolled vocabulary. We discriminates so-called subjective tags like "cool" and "fun" from folksonomy tags without any extra knowledge other than folksonomy triples and use the level of tag generalization to form the objective tags into a hierarchy. We verify that entropy of folksonomy tags is an effective measure for discriminating subjective folksonomy tags. Our hierarchical tag allocation method guarantees the number of children nodes and increases the number of available paths to a target node compared to an existing tree allocation method for folksonomy tags.The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tagshttp://www.bibsonomy.org/bibtex/2d44d1c9a48f5b676388ffbc90c7577ba/folkefolke2012-02-06T15:42:31+01:00analysis entropy folksonomy tagging <span class="authorEditorList"><a href="/author/Chi">Ed H. Chi</a>, and <a href="/author/Mytkowicz">Todd Mytkowicz</a> </span><em>Proceedings of the nineteenth ACM conference on Hypertext and hypermedia, </em><em>page 81--88. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Mon Feb 06 15:42:31 CET 2012New York, NY, USAProceedings of the nineteenth ACM conference on Hypertext and hypermedia81--88HT '08Understanding the efficiency of social tagging systems using information theory2008analysis entropy folksonomy tagging Given the rise in popularity of social tagging systems, it seems only natural to ask how efficient is the organically evolved tagging vocabulary in describing underlying document objects? Does this distributed process really provide a way to circumnavigate the traditional "vocabulary problem" with ontology? We analyze a social tagging site, namely del.icio.us, with information theory in order to evaluate the efficiency of this social tagging site for encoding navigation paths to information sources. We show that information theory provides a natural and interesting way to understand this efficiency - or the descriptive, encoding power of tags. Our results indicate the efficiency of tags appears to be waning. We discuss the implications of our findings and provide insight into how our methods can be used to design more usable social tagging software.Understanding the efficiency of social tagging systems using information theoryhttp://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/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/2a63b845d723820eda8d1e1cb7726441c/beatebeate2012-01-29T11:46:30+01:00folksonomy ranking social-search <span class="authorEditorList"><a href="/author/Abel">Fabian Abel</a>, <a href="/author/Henze">Nicola Henze</a>, and <a href="/author/Krause">Daniel Krause</a> </span><em>Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, Napa Valley, California, USA, October 26-30, 2008, </em><em>page 1429-1430. </em><em>ACM, </em>(<em>2008</em>)Sun Jan 29 11:46:30 CET 2012Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, Napa Valley, California, USA, October 26-30, 20081429-1430{R}anking in {F}olksonomy {S}ystems: can context help?2008folksonomy ranking social-search http://www.bibsonomy.org/bibtex/275e894c497d0584dcedeeb281d9bd263/maxirichtermaxirichter2012-01-18T14:24:29+01:00folksonomy relation tag tagging <span class="authorEditorList"><a href="/author/www.slideshare.net"> www.slideshare.net</a> </span>(<em>2012</em>)Wed Jan 18 14:24:29 CET 2012Stop thinking, start tagging - Tag Semantics emerge from Collaborative Verbosity2012folksonomy relation tag tagging http://www.bibsonomy.org/bibtex/2c5fd5d4042251d7a8654285aa20451ec/janus235janus2352012-01-17T14:16:46+01:00based clustering folksonomy graph <span class="authorEditorList"><a href="/author/Papadopoulos">Symeon Papadopoulos</a>, <a href="/author/Kompatsiaris">Yiannis Kompatsiaris</a>, and <a href="/author/Vakali">Athena Vakali</a> </span><em>Proceedings of the 12th international conference on Data warehousing and knowledge discovery, </em><em>page 65--76. </em><em>Berlin, Heidelberg, </em><em>Springer-Verlag, </em>(<em>2010</em>)Tue Jan 17 14:16:46 CET 2012Berlin, HeidelbergProceedings of the 12th international conference on Data warehousing and knowledge discovery65--76DaWaK'10A graph-based clustering scheme for identifying related tags in folksonomies2010based clustering folksonomy graph The paper presents a novel scheme for graph-based clustering with the goal of identifying groups of related tags in folksonomies. The proposed scheme searches for core sets, i.e. groups of nodes that are densely connected to each other by efficiently exploring the two-dimensional core parameter space, and successively expands the identified cores by maximizing a local subgraph quality measure. We evaluate this scheme on three real-world tag networks by assessing the relatedness of same-cluster tags and by using tag clusters for tag recommendation. In addition, we compare our results to the ones derived from a baseline graph-based clustering method and from a popular modularity maximization clustering method.A graph-based clustering scheme for identifying related tags in folksonomieshttp://www.bibsonomy.org/bibtex/2c5fd5d4042251d7a8654285aa20451ec/folkefolke2012-01-17T11:19:37+01:00community detection folksonomy local modularity <span class="authorEditorList"><a href="/author/Papadopoulos">Symeon Papadopoulos</a>, <a href="/author/Kompatsiaris">Yiannis Kompatsiaris</a>, and <a href="/author/Vakali">Athena Vakali</a> </span><em>Proceedings of the 12th international conference on Data warehousing and knowledge discovery, </em><em>page 65--76. </em><em>Berlin, Heidelberg, </em><em>Springer-Verlag, </em>(<em>2010</em>)Tue Jan 17 11:19:37 CET 2012Berlin, HeidelbergProceedings of the 12th international conference on Data warehousing and knowledge discovery65--76DaWaK'10A graph-based clustering scheme for identifying related tags in folksonomies2010community detection folksonomy local modularity The paper presents a novel scheme for graph-based clustering with the goal of identifying groups of related tags in folksonomies. The proposed scheme searches for core sets, i.e. groups of nodes that are densely connected to each other by efficiently exploring the two-dimensional core parameter space, and successively expands the identified cores by maximizing a local subgraph quality measure. We evaluate this scheme on three real-world tag networks by assessing the relatedness of same-cluster tags and by using tag clusters for tag recommendation. In addition, we compare our results to the ones derived from a baseline graph-based clustering method and from a popular modularity maximization clustering method.A graph-based clustering scheme for identifying related tags in folksonomieshttp://www.bibsonomy.org/bibtex/2f533eb306dec2071960c992e96086029/tinitini2012-01-07T14:39:42+01:00folksonomy <span class="authorEditorList"><a href="/author/Bertram">Jutta Bertram</a> </span><em>Information - Wissenschaft \&amp; Praxis</em> <em>60(1):19-26</em> (<em>2009</em>)Sat Jan 07 14:39:42 CET 2012Information - Wissenschaft \& Praxis119-26Social Tagging. Zum Potential einer neuen Indexiermethode602009folksonomy DABI. Ausgabe BibTeX.http://www.bibsonomy.org/bibtex/2ebe3e2483a76ff572d1ce109969e45c8/beatebeate2011-12-26T11:12:19+01:00document-indexing folksonomy tagging <span class="authorEditorList"><a href="/author/Voss">Jakob Voss</a> </span><em>CoRR</em> (<em>2007</em>)Mon Dec 26 11:12:19 CET 2011CoRRTagging, Folksonomy {\&} Co - Renaissance of Manual Indexing?abs/cs/07010722007document-indexing folksonomy tagging DBLP BibTeX Record 'journals/corr/abs-cs-0701072'http://www.bibsonomy.org/bibtex/2f022e60c5928e01c701d7ec539ec221b/jaeschkejaeschke2011-12-21T22:52:09+01:00bookmarking collaborative folkrank folksonomy ranking search tagging web pagerank <span class="authorEditorList"><a href="/author/Kim">Heung-Nam Kim</a>, and <a href="/author/El Saddik">Abdulmotaleb El Saddik</a> </span><em>Proceedings of the fifth ACM conference on Recommender systems, </em><em>page 45--52. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2011</em>)Wed Dec 21 22:52:09 CET 2011New York, NY, USAProceedings of the fifth ACM conference on Recommender systems45--52Personalized PageRank vectors for tag recommendations: inside FolkRank2011bookmarking collaborative folkrank folksonomy ranking search tagging web pagerank This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.http://www.bibsonomy.org/bibtex/2af3f6ceb98d1d26172b3d0c00dee4a2a/beatebeate2011-12-21T21:59:59+01:00folksonomy power-law preferential-attachment tag-recommendation tagging <span class="authorEditorList"><a href="/author/Bollen">Dirk Bollen</a>, and <a href="/author/Halpin">Harry Halpin</a> </span><em>Proceedings of the 20th ACM conference on Hypertext and hypermedia, </em><em>page 359--360. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2009</em>)Wed Dec 21 21:59:59 CET 2011New York, NY, USAProceedings of the 20th ACM conference on Hypertext and hypermedia359--360HT '09The role of tag suggestions in folksonomies2009folksonomy power-law preferential-attachment tag-recommendation tagging Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. We present experimental results that show that the power law distribution forms regardless of whether or not tag suggestions are presented to the users.The role of tag suggestions in folksonomieshttp://www.bibsonomy.org/bibtex/2f1ae7613a8939b365d469e2a31163d56/beatebeate2011-12-21T21:35:33+01:00folksonomy power-law tagging <span class="authorEditorList"><a href="/author/Li">Xin Li</a>, <a href="/author/Guo">Lei Guo</a>, and <a href="/author/Zhao">Yihong Eric Zhao</a> </span><em>Proceedings of the 17th international conference on World Wide Web, </em><em>page 675--684. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Wed Dec 21 21:35:33 CET 2011New York, NY, USAProceedings of the 17th international conference on World Wide Web675--684WWW '08Tag-based social interest discovery2008folksonomy power-law tagging The success and popularity of social network systems, such as del.icio.us, Facebook, MySpace, and YouTube, have generated many interesting and challenging problems to the research community. Among others, discovering social interests shared by groups of users is very important because it helps to connect people with common interests and encourages people to contribute and share more contents. The main challenge to solving this problem comes from the difficulty of detecting and representing the interest of the users. The existing approaches are all based on the online connections of users and so unable to identify the common interest of users who have no online connections.</p> <p>In this paper, we propose a novel social interest discovery approach based on user-generated tags. Our approach is motivated by the key observation that in a social network, human users tend to use descriptive tags to annotate the contents that they are interested in. Our analysis on a large amount of real-world traces reveals that in general, user-generated tags are consistent with the web content they are attached to, while more concise and closer to the understanding and judgments of human users about the content. Thus, patterns of frequent co-occurrences of user tags can be used to characterize and capture topics of user interests. We have developed an Internet Social Interest Discovery system, ISID, to discover the common user interests and cluster users and their saved URLs by different interest topics. Our evaluation shows that ISID can effectively cluster similar documents by interest topics and discover user communities with common interests no matter if they have any online connections.Tag-based social interest discoveryhttp://www.bibsonomy.org/bibtex/270a32e6bbe65fbf0cf28aea4da68aacd/tamari-555tamari-5552011-12-20T17:28:20+01:00Web2.0 folksonomy wissensrepresentation <span class="authorEditorList"><a href="/author/Peters">Isabella Peters</a>, and <a href="/author/Stock">Wolfgang G. Stock</a> </span><em>Information - Wissenschaft und Praxis</em> <em>59(2):77--90</em> (<em>2008</em>)Tue Dec 20 17:28:20 CET 2011Information - Wissenschaft und Praxis277--90Folksonomies in Wissensrepräsentation und Information Retrieval592008Web2.0 folksonomy wissensrepresentation Folksonomies in Wissensrepräsentation und Information Retrieval. Die populären Web 2.0-Dienste werden von Prosumern – Produzenten und gleichsam Konsumenten – nicht nur dazu genutzt, Inhalte zu produzieren, sondern auch, um sie inhaltlich zu erschließen. Folksonomies erlauben es dem Nutzer, Dokumente mit eigenen Schlagworten, sog. Tags, zu beschreiben, ohne dabei auf gewisse Regeln oder Vorgaben achten zu müssen. Neben einigen Vorteilen zeigen Folksonomies aber auch zahlreiche Schwächen (u. a. einen Mangel an Präzision). Um diesen Nachteilen größtenteils entgegenzuwirken, schlagen wir eine Interpretation der Tags als natürlichsprachige Wörter vor. Dadurch ist es uns möglich, Methoden des Natural Language Processing (NLP) auf die Tags anzuwenden und so linguistische Probleme der Tags zu beseitigen. Darüber hinaus diskutieren wir Ansätze und weitere Vorschläge (Tagverteilungen, Kollaboration und akteurspezifische Aspekte) hinsichtlich eines Relevance Rankings von getaggten Dokumenten. Neben Vorschlägen auf ähnliche Dokumente („more like this!“) erlauben Folksonomies auch Hinweise auf verwandte Nutzer und damit auf Communities („more like me!“). Folksonomies in Knowledge Representation and Information Retrieval In Web 2.0 services “prosumers” – producers and consumers – collaborate not only for the purpose of creating content, but to index these pieces of information as well. Folksonomies permit actors to describe documents with subject headings, “tags“, without regarding any rules. Apart from a lot of benefits folksonomies have many shortcomings (e.g., lack of precision). In order to solve some of the problems we propose interpreting tags as natural language terms. Accordingly, we can introduce methods of NLP to solve the tags’ linguistic problems. Additionally, we present criteria for tagged documents to create a ranking by relevance (tag distribution, collaboration and actor-based aspects). Besides recommending similar documents („more like this!“) folksonomies can be used for the recommendation of similar users and communities („more like me!“).http://www.bibsonomy.org/bibtex/2b73bf457ee6d8306c103a4ce8ae13ede/beatebeate2011-12-19T22:35:07+01:00folksonomy literature overview semantics tagging <span class="authorEditorList"><a href="/author/Trant">Jennifer Trant</a> </span><em>Journal of Digital Information</em> (<em>2009</em>)Mon Dec 19 22:35:07 CET 2011Journal of Digital Information1Studying Social Tagging and Folksonomy: A Review and Framework102009folksonomy literature overview semantics tagging This paper reviews research into social tagging and folksonomy (as reflected in about 180 sources published through December 2007). Methods of researching the contribution of social tagging and folksonomy are described, and outstanding research questions are presented. This is a new area of research, where theoretical perspectives and relevant research methods are only now being defined. This paper provides a framework for the study of folksonomy, tagging and social tagging systems. Three broad approaches are identified, focusing first, on the folksonomy itself (and the role of tags in indexing and retrieval); secondly, on tagging (and the behaviour of users); and thirdly, on the nature of social tagging systems (as socio-technical frameworks).http://www.bibsonomy.org/bibtex/2da6c676c5664017247c7564fc247b190/beatebeate2011-12-18T18:48:14+01:00folksonomy network-properties tagging <span class="authorEditorList"><a href="/author/Cattuto">Ciro Cattuto</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, <a href="/author/Baldassarri">Andrea Baldassarri</a>, <a href="/author/Servedio">Vito D. P. Servedio</a>, <a href="/author/Loreto">Vittorio Loreto</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Grahl">Miranda Grahl</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering&#039;&#039;</em> <em>20(4):245-262</em> (<em>2007</em>)Sun Dec 18 18:48:14 CET 2011AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering''4245-262Network Properties of Folksonomies202007folksonomy network-properties tagging See http://www.bibsonomy.org/bibtex/2e1a5234a896b1f422473b1fe5d91e26b/stumme for a shorter workshop version.http://www.bibsonomy.org/bibtex/2857ef9d1c4704b459f218fa967056daf/beatebeate2011-12-17T18:07:21+01:00characteristics folksonomy tagging <span class="authorEditorList"><a href="/author/Quintarelli">Emanuele Quintarelli</a> </span>(<em>June 2005</em>)Sat Dec 17 18:07:21 CET 2011JuneFolksonomies: power to the people2005characteristics folksonomy tagging In recent times, an unprecedented amount of Web content has begun to be generated through web logs, wikis and other social tools thanks to lower technology and cost barriers. A new host of content creators is emerging, often individuals with the will to participate in discussions and share their ideas with like-minded people. This is to say that this increasing amount of varied, valuable content is generated by non-trained, non-expert information professionals: they are at the same time users and producers of information. We have gone past a critical mass of connectivity between people that has introduced a new revolutionary ability to communicate, collaborate and share goods online. To respond to these increased informational and exchange needs, new communication models are emerging and producing an incredible amount of distributed information that information management professionals, information architects, librarians and knowledge workers at large need to link, aggregate, and organize in order to extract knowledge. The issue is whether the traditional organizational schemes used so far are suitable to address the classification needs of fast-proliferating, new information sources or if, to achieve this goal, better aggregation and concept matching tools are required. Folksonomies attempt to provide a solution to this issue, by introducing an innovative distributed approach based on social classification.