descriptions of single articles. These can either be represented by
author-generated keywords, professionally indexed subject
headings, automatically extracted terms or by reader-generated
tags as used in social bookmarking systems. It is assumed that
particularly the users? view on article content differs significantly
from the authors? or indexers? perspectives. To verify this
assumption, title and abstract terms, author keywords, Inspec
subject headings, KeyWords PlusTM and tags are compared by
calculating the overlap between the respective datasets. Our
approach includes extensive term preprocessing (i.e. stemming,
spelling unifications) to gain a homogeneous term collection.
When term overlap is calculated for every single document of the
dataset, similarity values are low. Thus, the presented study
confirms the assumption, that the different types of keywords
each reflect a different perspective of the articles? contents and
that tags (cumulated across articles) can be used in journal
evaluation to represent a reader-specific view on published
content. ER - TY - CONF AU - Venetis, Petros AU - Koutrika, Georgia AU - Garcia-Molina, Hector A2 - T1 - On the selection of tags for tag clouds T2 - Proceedings of the fourth ACM international conference on Web search and data mining PB - ACM C1 - New York, NY, USA PY - 2011/ CY - VL - IS - SP - 835 EP - 844 UR - http://doi.acm.org/10.1145/1935826.1935855 DO - 10.1145/1935826.1935855 KW - selection KW - tagcloud KW - tags KW - toread L1 - SN - 978-1-4503-0493-1 N1 - On the selection of tags for tag clouds N1 - AB - We examine the creation of a tag cloud for exploring and understanding a set of objects (e.g., web pages, documents). In the first part of our work, we present a formal system model for reasoning about tag clouds. We then present metrics that capture the structural properties of a tag cloud, and we briefly present a set of tag selection algorithms that are used in current sites (e.g., del.icio.us, Flickr, Technorati) or that have been described in recent work. In order to evaluate the results of these algorithms, we devise a novel synthetic user model. This user model is specifically tailored for tag cloud evaluation and assumes an "ideal" user. We evaluate the algorithms under this user model, as well as the model itself, using two datasets: CourseRank (a Stanford social tool containing information about courses) and del.icio.us (a social bookmarking site). The results yield insights as to when and why certain selection schemes work best. ER - TY - GEN AU - Garcia-Silva, A. AU - Corcho, Oscar AU - Gracia, J. A2 - T1 - Associating Semantics to Multilingual Tags in Folksonomies JO - PB - C1 - PY - 2010/ VL - IS - SP - EP - UR - http://oa.upm.es/5646/ DO - KW - multilingual KW - ol_web2.0 KW - semantics KW - tags KW - enrichment KW - tag_concept_mapping L1 - N1 - Associating Semantics to Multilingual Tags in Folksonomies - Archivo Digital UPM N1 - AB - Tagging systems are nowadays a common feature in web sites where user-generated content plays an important role. However, the lack of semantics and multilinguality hamper information retrieval process based on folksonomies. In this paper we propose an approach to bring semantics to multilingual folksonomies. This approach includes a sense disambiguation activity and takes advantage from knowledge generated by the masses in the form of articles, redirection and disambiguation links, and translations in Wikipedia. We use DBpedia[2] as semantic resource to define the tag meanings. ER - TY - JOUR AU - Gedikli, Fatih AU - Jannach, Dietmar T1 - Rating items by rating tags JO - Systems and the Social Web at ACM PY - 2010/ VL - IS - SP - EP - UR - DO - KW - item KW - rating KW - recommender KW - tags L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Carman, Mark J. AU - Baillie, Mark AU - Gwadera, Robert AU - Crestani, Fabio A2 - T1 - A statistical comparison of tag and query logs T2 - Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval PB - ACM C1 - New York, NY, USA PY - 2009/ CY - VL - IS - SP - 123 EP - 130 UR - http://doi.acm.org/10.1145/1571941.1571965 DO - 10.1145/1571941.1571965 KW - comparison KW - diagram KW - divergence KW - kullblack-Leibler KW - mining KW - tags KW - triangular KW - vocabulary KW - weblog L1 - SN - 978-1-60558-483-6 N1 - A statistical comparison of tag and query logs N1 - AB - We investigate tag and query logs to see if the terms people use to annotate websites are similar to the ones they use to query for them. Over a set of URLs, we compare the distribution of tags used to annotate each URL with the distribution of query terms for clicks on the same URL. Understanding the relationship between the distributions is important to determine how useful tag data may be for improving search results and conversely, query data for improving tag prediction. In our study, we compare both term frequency distributions using vocabulary overlap and relative entropy. We also test statistically whether the term counts come from the same underlying distribution. Our results indicate that the vocabulary used for tagging and searching for content are similar but not identical. We further investigate the content of the websites to see which of the two distributions (tag or query) is most similar to the content of the annotated/searched URL. Finally, we analyze the similarity for different categories of URLs in our sample to see if the similarity between distributions is dependent on the topic of the website or the popularity of the URL. ER - TY - CONF AU - Giannakidou, Eirini AU - Koutsonikola, Vassiliki A. AU - Vakali, Athena AU - Kompatsiaris, Yiannis A2 - T1 - Co-Clustering Tags and Social Data Sources. T2 - WAIM PB - IEEE C1 - PY - 2008/ CY - VL - IS - SP - 317 EP - 324 UR - http://dblp.uni-trier.de/db/conf/waim/waim2008.html#GiannakidouKVK08 DO - KW - clustering KW - ol_web2.0 KW - tags KW - methods_concepts L1 - SN - 978-0-7695-3185-4 N1 - dblp N1 - AB - Under social tagging systems, a typical Web 2.0 application, users label digital data sources by using freely chosen textual descriptions (tags). Poor retrieval in the aforementioned systems remains a major problem mostly due to questionable tag validity and tag ambiguity. Earlier clustering techniques have shown limited improvements, since they were based mostly on tag co-occurrences. In this paper, a co-clustering approach is employed, that exploits joint groups of related tags and social data sources, in which both social and semantic aspects of tags are considered simultaneously. Experimental results demonstrate the efficiency and the beneficial outcome of the proposed approach in correlating relevant tags and resources. ER - TY - CONF AU - Grineva, Maria AU - Grinev, Maxim AU - Turdakov, Denis AU - Velikhov, Pavel A2 - T1 - Harnessing Wikipedia for Smart Tags Clustering T2 - Proceedings of the International Workshop on Knowledge Acquisition from the Social Web (KASW2008) PB - C1 - PY - 2008/ CY - VL - IS - SP - EP - UR - DO - KW - clustering KW - ol_web2.0 KW - tags KW - wikipedia KW - methods_concepts KW - data_wikis L1 - SN - N1 - N1 - AB - The quality of the current tagging services can be greatly improved if the service is able to cluster tags by their meaning. Tag clouds clustered by higher level topics enable the users to explore their tag space, which is especially needed when tag clouds become large. We demonstrate TagCluster - a tool for automated tag clustering that harnesses knowledge from Wikipedia about semantic relatedness between tags and names of categories to achieve smart clustering. Our approach shows much better quality of clusters compared to the existing techniques that rely on tag co-occurrence analysis in the tagging service. ER - TY - THES AU - Illig, Jens T1 - Machine Learnability Analysis of Textclassifications in a Social Bookmarking Folksonomy PY - 2008/ PB - University of Kassel SP - EP - UR - DO - KW - classification KW - learning KW - machine KW - recommendations KW - recommender KW - tags L1 - N1 - N1 - AB - ER - TY - JOUR AU - Jäschke, Robert AU - Marinho, Leandro AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd T1 - Tag recommendations in social bookmarking systems JO - AI Communications PY - 2008/01 VL - 21 IS - 4 SP - 231 EP - 247 UR - http://dx.doi.org/10.3233/AIC-2008-0438 DO - 10.3233/AIC-2008-0438 KW - recommendation KW - social_bookmarking KW - tagging KW - tags L1 - SN - N1 - Tag recommendations in social bookmarking systems - AI Communications - Volume 21, Number 4 / 2008 - IOS Press N1 - AB - Collaborative tagging systems allow users to assign keywords – so called “tags” – to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurrences. We show that both FolkRank and collaborative filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. ER - TY - JOUR AU - Jäschke, Robert AU - Marinho, Leandro AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd T1 - Tag Recommendations in Social Bookmarking Systems JO - AI Communications PY - 2008/ VL - 21 IS - 4 SP - 231 EP - 247 UR - http://dx.doi.org/10.3233/AIC-2008-0438 DO - 10.3233/AIC-2008-0438 KW - 2.0 KW - 2008 KW - Recommendations KW - bookmarking KW - itegpub KW - logsonomies KW - myown KW - recommendations KW - recommender KW - social KW - systems KW - tag KW - tagorapub KW - tags KW - web KW - web2.0 KW - web20 L1 - SN - N1 - N1 - AB - Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
ER - TY - JOUR AU - Jäschke, Robert AU - Marinho, Leandro AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd T1 - Tag Recommendations in Social Bookmarking Systems JO - AI Communications PY - 2008/ VL - 21 IS - 4 SP - 231 EP - 247 UR - http://dx.doi.org/10.3233/AIC-2008-0438 DO - 10.3233/AIC-2008-0438 KW - 2.0 KW - 2008 KW - Recommendations KW - bookmarking KW - itegpub KW - logsonomies KW - myown KW - recommendations KW - recommender KW - social KW - systems KW - tag KW - tagorapub KW - tags KW - web KW - web2.0 KW - web20 L1 - SN - N1 - N1 - AB - Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
ER - TY - JOUR AU - Maala, Mohamed Zied AU - Delteil, Alexandre AU - Azough, Ahmed T1 - A conversion process from Flickr tags to RDF descriptions JO - IADIS INTERNATIONAL JOURNAL ON WWW/INTERNET PY - 2008/ VL - 6 IS - 1 SP - EP - UR - http://liris.cnrs.fr/publis/?id=4425 DO - KW - conversion KW - ol_web2.0 KW - rdf KW - tags KW - tag_concept_mapping L1 - SN - N1 - publis — LIRIS N1 - AB - The recent evolution of the Web, now designated by the term Web 2.0, has seen the appearance of a huge number of resources created and annotated by users. However the annotations consist only in simple tags that are gathered in unstructured sets called folksonomies. The use of more complex languages to annotate resources and to define semantics according to the vision of the Semantic Web, would improve the understanding by machines and programs, like search engines, of what is on the Web. Indeed tags expressivity is very low compared to the representation standards of the Semantic Web, like RDF and OWL. But users appear to be still reluctant to annotate resources with RDF, and it should be recognized that Semantic Web, contrary to Web 2.0, is still not a reality of today’s Web. One way to take advantage of Semantic Web capabilities right now, without waiting for a change of the annotation usages, would be to be able to generate RDF annotations from tags. As a first step toward this direction, this paper presents a tentative to automatically convert a set of tags into a RDF description in the context of photos on Flickr. Such a method exploits some specificity of tags used on Flickr, some basic natural language processing tools and some semantic resources, in order to relate semantically tags describing a given photo and build a pertinent RDF annotation for this photo. ER - TY - CONF AU - Al-Khalifa, Hend S. AU - Davis, Hugh C. A2 - T1 - Towards better understanding of folksonomic patterns T2 - Proceedings of the eighteenth conference on Hypertext and hypermedia PB - ACM C1 - New York, NY, USA PY - 2007/ CY - VL - IS - SP - 163 EP - 166 UR - http://doi.acm.org/10.1145/1286240.1286288 DO - http://doi.acm.org/10.1145/1286240.1286288 KW - analysis KW - tagging KW - tags L1 - SN - 978-1-59593-820-6 N1 - N1 - AB - ER - TY - JOUR AU - Al-Khalifa, Hend S. AU - Davis, Hugh C. T1 - Folksonomies versus Automatic Keyword Extraction: An Empirical Study JO - IADIS INTERNATIONAL JOURNAL ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (IJCSIS) PY - 2006/october VL - Vol. 1 IS - Number SP - 132 EP - 143 UR - http://eprints.ecs.soton.ac.uk/13155/ DO - KW - comparison KW - folksonomies KW - keywords KW - ol_web2.0 KW - study KW - tags KW - emergent_semantics L1 - SN - N1 - N1 - AB - Semantic Metadata, which describes the meaning of documents, can be produced either manually or else semi-automatically using information extraction techniques. Manual techniques are expensive if they rely on skilled cataloguers, but a possible alternative is to make use of community produced annotations such as those collected in folksonomies. This paper reports on an experiment that we carried out to validate the assumption that folksonomies contain higher semantic value than keywords extracted by machines. The experiment has been carried-out in two ways: subjectively, by asking a human indexer to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The result of the experiment can be considered as evidence for the rich semantics of folksonomies, demonstrating that folksonomies used in the del.icio.us bookmarking service can be used in the process of generating semantic metadata to annotate web resources. ER - TY - CONF AU - Dubinko, Micah AU - Kumar, Ravi AU - Magnani, Joseph AU - Novak, Jasmine AU - Raghavan, Prabhakar AU - Tomkins, Andrew A2 - T1 - Visualizing tags over time T2 - WWW '06: Proceedings of the 15th international conference on World Wide Web PB - ACM Press C1 - New York, NY, USA PY - 2006/ CY - VL - IS - SP - 193 EP - 202 UR - http://dx.doi.org/10.1145/1135777.1135810 DO - 10.1145/1135777.1135810 KW - folksonomy KW - tagging KW - tags KW - time KW - visualization KW - visualizing L1 - SN - 1595933239 N1 - djsaab's CiteULike library 20091211 N1 - AB - ER - TY - JOUR AU - Golder, Scott A. AU - Huberman, Bernardo A. T1 - Usage patterns of collaborative tagging systems JO - Journal of Information Science PY - 2006/ VL - 32 IS - 2 SP - 198 EP - 208 UR - http://jis.sagepub.com/content/32/2/198.abstract DO - 10.1177/0165551506062337 KW - golder KW - patterns KW - tagging KW - tags KW - usage L1 - SN - N1 - Usage patterns of collaborative tagging systems N1 - AB - Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge. ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - T1 - Das Entstehen von Semantik in BibSonomy T2 - Social Software in der Wertschöpfung PB - Nomos C1 - Baden-Baden PY - 2006/ CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006entstehen.pdf DO - KW - tags KW - semantik KW - 2006 KW - pagerank KW - tagging KW - association KW - hotho KW - bibsonomy KW - schmitz KW - jaeschke KW - rules KW - semantics KW - stumme KW - BibSonomy KW - nepomuk KW - folksonomy KW - tagorapub KW - folksonomies KW - tagora KW - folkrank KW - UniK KW - ol_web2.0 KW - emergentsemantics_evidence L1 - E:\home\help_of_all_helps.pdf SN - N1 - N1 - AB - Immer mehr Soziale-Lesezeichen-Systeme entstehen im heutigen Web. In solchen Systemen erstellen die Nutzer leichtgewichtige begriffliche Strukturen, so genannte Folksonomies. Ihren Erfolg verdanken sie der Tatsache, dass man keine speziellen Fähigkeiten benötigt, um an der Gestaltung mitzuwirken. In diesem Artikel beschreiben wir unser System BibSonomy. Es erlaubt das Speichern, Verwalten und Austauschen sowohl von Lesezeichen (Bookmarks) als auch von Literaturreferenzen in Form von BibTeX-Einträgen. Die Entwicklung des verwendeten Vokabulars und der damit einhergehenden Entstehung einer gemeinsamen Semantik wird detailliert diskutiert. ER -