BibSonomyburst/user/ht09/taggingBibSonomy RSS feed for /user/ht09/tagging2012-02-16T04:37:22+01:00http://www.bibsonomy.org/bibtex/21ec9c6b266af3bd0ce0c67f6f38983dd/ht09ht092009-06-16T15:00:02+02:00ht2009 semiotics tagging w4tag workshop <span class="authorEditorList"><a href="/author/Loreto">Vittorio Loreto</a>, and <a href="/author/Capocci">Andrea Capocci</a> </span><em>HT &#039;09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>July 2009</em>)Tue Jun 16 15:00:02 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyTagging Dynamics in Online Communities2009ht2009 semiotics tagging w4tag workshop http://www.bibsonomy.org/bibtex/2afbf2fd02327dd08c464c708d7a737a9/ht09ht092009-06-16T15:00:02+02:00automatic extraction ht2009 hypertext information legal semantic shortPaper sp75 tagging <span class="authorEditorList"><a href="/author/Lesmo">Leonardo Lesmo</a>, <a href="/author/Mazzei">Alessandro Mazzei</a>, and <a href="/author/Radicioni">Daniele P. Radicioni</a> </span><em>HT &#039;09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>July 2009</em>)Tue Jun 16 15:00:02 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyExtracting Semantic Annotations from Legal Texts2009automatic extraction ht2009 hypertext information legal semantic shortPaper sp75 tagging This paper illustrates a system designed to automatically extract semantic annotations of the normative modifications present in legal texts. The work relies on a deep parsing approach. The problem of semantically annotating legal texts is cast to the problem of mapping parse trees to semantic frames representing such modifications. We report a preliminary experimentation along with the dataset employed, and discuss the results to point out future improvements.http://www.bibsonomy.org/bibtex/2d7b14a0eb7fabb3cee8846802de069fe/ht09ht092009-06-16T15:00:02+02:00ht2009 laws poster power pp161 suggestions tag tagging <span class="authorEditorList"><a href="/author/Bollen">Dirk Bollen</a>, and <a href="/author/Halpin">Harry Halpin</a> </span><em>HT &#039;09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>July 2009</em>)Tue Jun 16 15:00:02 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyThe Role of Tag Suggestions in Folksonomies2009ht2009 laws poster power pp161 suggestions tag 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.http://www.bibsonomy.org/bibtex/28be99078789359235d781eccfa2902c7/ht09ht092009-06-16T15:00:02+02:00aid hot ht2009 language natural navigation poster pp154 processing semantic tagging topics web2.0 <span class="authorEditorList"><a href="/author/Bruckschen">Mírian Bruckschen</a>, <a href="/author/Vieira">Renata Vieira</a>, and <a href="/author/Rigo">Sandro Rigo</a> </span><em>HT &#039;09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>July 2009</em>)Tue Jun 16 15:00:02 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyNamed Entities for Hot Topics Ranking and Ontology Navigation Aid2009aid hot ht2009 language natural navigation poster pp154 processing semantic tagging topics web2.0 This paper presents an application which relies on Natural Language Processing techniques to identify hot topics in a news portal. Named Entity Recognition, semantic tagging and identity identification are used to acquire knowledge of the domain, generate ontologies automatically and rank hot topics for the news in the portal.http://www.bibsonomy.org/bibtex/2f696989e22dd4c77c8a6352526e13efe/ht09ht092009-06-16T15:00:02+02:00components connected detection fp105 fullPaper ht2009 spam tagging <span class="authorEditorList"><a href="/author/Neubauer">Nicolas Neubauer</a>, and <a href="/author/Obermayer">Klaus Obermayer</a> </span><em>HT &#039;09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>July 2009</em>)Tue Jun 16 15:00:02 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyHyperincident Connected Components of Tagging Networks2009components connected detection fp105 fullPaper ht2009 spam tagging Data created by social bookmarking systems can be described as 3-partite 3-uniform hypergraphs connecting documents, users, and tags (tagging networks), such that the toolbox of complex network analysis can be applied to examine their properties. One of the most basic tools, the analysis of connected components, however cannot be applied meaningfully: Tagging networks tend to be almost entirely connected. We therefore propose a generalization of connected components, m-hyperincident connected components. We show that decomposing tagging networks into 2-hyperincident connected components yields a characteristic component distribution with a salient giant component that can be found across various datasets. This pattern changes if the underlying formation process changes, for example, if the hypergraph is constructed from search logs, or if the tagging data is contaminated by spam: It turns out that the second- to 129th largest components of the spam-labeled Bibsonomy dataset are inhabited exclusively by spam users. Based on these findings, we propose and unsupervised method for spam detection. http://www.bibsonomy.org/bibtex/2ec87d0f3836518886ee1f4ecf6ffe9c8/ht09ht092009-06-16T15:00:02+02:00fp102 fullPaper ht2009 interest model null random reuse sharing tag tagging <span class="authorEditorList"><a href="/author/Santos-Neto">Elizeu Santos-Neto</a>, <a href="/author/Condon">David Condon</a>, <a href="/author/Andrade">Nazareno Andrade</a>, <a href="/author/Iamnitchi">Adriana Iamnitchi</a>, and <a href="/author/Ripeanu">Matei Ripeanu</a> </span><em>HT &#039;09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>July 2009</em>)Tue Jun 16 15:00:02 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyIndividual and Social Behavior in Tagging Systems2009fp102 fullPaper ht2009 interest model null random reuse sharing tag tagging In tagging systems users can annotate items of interest with free-form terms. A good understanding of usage characteristics of such systems is necessary to improve the design of current and next generation of tagging systems. To this end, this work explores three aspects of user behavior in CiteULike and Connotea, two systems that include tagging features to support online personalized management of scientific publications. First, this study characterizes the degree to which users re-tag previously published items and reuse tags: 10 to 20% of the daily activity can be characterized as re-tagging and about 75% of the activity as tag reuse. Second, we use the pairwise similarity between users activity to characterize the interest sharing in the system. We present the interest sharing distribution across the system, show that this metric encodes information about existing usage patterns, and attempt to correlate interest sharing levels to indicators of collaboration such as co-membership in discussion groups and semantic similarity of tag vocabularies. Finally, we show that interest sharing leads to an implicit structure that exhibit a natural segmentation. Throughout the paper we discuss the potential impact of our findings on the design of mechanisms that support tagging systems.http://www.bibsonomy.org/bibtex/29e7a2a323235eed80b2b91bbf2e659cd/ht09ht092009-06-16T15:00:02+02:00folksonomies fp056 fullPaper ht2009 information model models reduction retrieval search smoothing space sparseness tagging vector <span class="authorEditorList"><a href="/author/Abbasi">Rabeeh Abbasi</a>, and <a href="/author/Staab">Steffen Staab</a> </span><em>HT &#039;09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>July 2009</em>)Tue Jun 16 15:00:02 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyRichVSM: enRiched Vector Space Models for Folksonomies2009folksonomies fp056 fullPaper ht2009 information model models reduction retrieval search smoothing space sparseness tagging vector People share millions of resources (photos, bookmarks, videos, etc.) in Folksonomies (like Flickr, Delicious, Youtube, etc.). To access and share resources, they add keywords called tags to the resources. As the tags are freely chosen keywords, it might not be possible for users to tag their resources with all the relevant tags. As a result, many resources lack sufficient number of relevant tags. The lack of relevant tags results into sparseness of data, and this sparseness of data makes many relevant resources unsearchable against user queries. In this paper, we explore two dimensions of semantic relationships between tags, based on the context and the distribution of tags. We exploit semantic relationships between tags to reduce sparseness in Folksonomies and propose different enriched vector space models. We also propose a vector space model Best of Breed which utilizes appropriate enrichment method based on the type of the query. We evaluate the proposed methods on a large dataset of 27 million resources, 92 thousand tags and 94 million tag assignments. Experimental results show that the enriched vector space models help in improving search, especially for the rare queries which have few relevant resources in the sparse data.http://www.bibsonomy.org/bibtex/2f3000777d9b48c7a5e7499def8e45e8e/ht09ht092009-06-16T15:00:02+02:00collaborative context folksonomy fp017 fullPaper ht2009 semantics tagging <span class="authorEditorList"><a href="/author/man Au Yeung">Ching man Au Yeung</a>, <a href="/author/Gibbins">Nicholas Gibbins</a>, and <a href="/author/Shadbolt">Nigel Shadbolt</a> </span><em>HT &#039;09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>July 2009</em>)Tue Jun 16 15:00:02 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyContextualising Tags in Collaborative Tagging Systems2009collaborative context folksonomy fp017 fullPaper ht2009 semantics tagging Collaborative tagging systems are now popular tools for organising and sharing information on the Web. While collaborative tagging offers many advantages over the use of controlled vocabularies, they also suffer from problems such as the existence of polysemous tags. We investigate how the different contexts in which individual tags are used can be revealed automatically without consulting any external resources. We consider several different network representations of tags and documents, and apply a graph clustering algorithm on these networks to obtain groups of tags or documents corresponding to the different meanings of an ambiguous tag. Our experiments show that networks which explicitly take the social context into account are more likely to give a better picture of the semantics of a tag.http://www.bibsonomy.org/bibtex/2f77b248ed41194330e6d22180c742581/ht09ht092009-06-16T15:00:02+02:002.0 de143 demo folksonomy ht2009 tagging tools web <span class="authorEditorList"><a href="/author/Benz">Dominik Benz</a>, <a href="/author/Eisterlehner">Folke Eisterlehner</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Krause">Beate Krause</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>HT &#039;09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>July 2009</em>)Tue Jun 16 15:00:02 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyManaging Publications and Bookmarks with BibSonomy20092.0 de143 demo folksonomy ht2009 tagging tools web In this demo we present BibSonomy, a social bookmark and publication sharing system.http://www.bibsonomy.org/bibtex/260b2d9e5bc272c6a5cf6ccf8cc66481c/ht09ht092009-06-16T15:00:02+02:00a de140 demo games ht2009 purpose social tagging with <span class="authorEditorList"><a href="/author/Roinestad">Heather Roinestad</a>, <a href="/author/Burgoon">John Burgoon</a>, <a href="/author/Markines">Benjamin Markines</a>, and <a href="/author/Menczer">Filippo Menczer</a> </span><em>HT &#039;09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>July 2009</em>)Tue Jun 16 15:00:02 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyIncentives for Social Annotation2009a de140 demo games ht2009 purpose social tagging with Researchers are exploring the use of folksonomies, such as in social bookmarking systems, to build implicit links between online resources. Users create and reinforce links between resources through applying a common tag to those resources. The effectiveness of using such community-driven annotation depends on user participation to provide the critical information. However, the participation of many users is motivated by selfish reasons. An effective way to encourage these users is to create useful or entertaining applications. We demo two such tools -- a browser extension for bookmark management and navigation and a game.http://www.bibsonomy.org/bibtex/2393836d4c52c89783a7e33ddc116f47f/ht09ht092009-06-16T15:00:02+02:00bookmarking co-occurrence de130 demo ht2009 network search social tag tagging <span class="authorEditorList"><a href="/author/Marcellin">Lara Marcellin</a>, and <a href="/author/Politi">Roberto Politi</a> </span><em>HT &#039;09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>July 2009</em>)Tue Jun 16 15:00:02 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyTag Vision: Social Knowledge for Collaborative Search2009bookmarking co-occurrence de130 demo ht2009 network search social tag tagging In this paper we describe a model of social and collaborative search based on the use of tags. First we will introduce the issues that drove us to the definition of this model, analyzing different elements characterizing the web 2.0 paradigm and their effect on traditional search and classification systems. Afterwards we will present TAG Vision, a prototype implementation of the model, developed in order to investigate new approaches for information retrieval.