BibSonomy publications for /group/tagora/tagorapubFri Nov 25 12:41:06 CET 2011Berlin/HeidelbergKnowledge Processing and Data Analysis136--149Lecture Notes in Computer ScienceA Comparison of Content-Based Tag Recommendations in Folksonomy Systems658120112011 content folksonomy itegpub l3s myown recommendations recommender tag tagorapub Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. Thu Feb 17 17:42:47 CET 201118th International World Wide Web ConferenceApril641--641Evaluating Similarity Measures for Emergent Semantics of Social Tagging20092009 itegpub methods_concepts myown ol_web2.0 semantic_relatedness social_similarity taggingsurvey tagorapub www2009 Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We ?nd that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.Thu Feb 17 17:42:29 CET 2011Baden-BadenSocial Software in der WertschöpfungDas Entstehen von Semantik in BibSonomy20062006 BibSonomy UniK association bibsonomy emergentsemantics_evidence folkrank folksonomies folksonomy hotho jaeschke nepomuk ol_web2.0 pagerank rules schmitz semantics semantik stumme tagging taggingsurvey tagora tagorapub tags 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.Thu Feb 17 17:42:27 CET 2011BonnInformatik 2006 -- Informatik für Menschen. Band 2OctoberProc. Workshop on Applications of Semantic Technologies, Informatik 2006Lecture Notes in InformaticsEmergent Semantics in BibSonomyP-9420062006 UniK bibsonomy emergence emergent emergentsemantics_evidence folksonomy hotho itegpub jaeschke l3s nepomuk ol_web2.0 schmitz semantics stumme taggingsurvey tagorapub Social bookmark tools are rapidly emerging on the Web. In suchsystems users are setting up lightweight conceptual structurescalled folksonomies. The reason for their immediate success is thefact that no specific skills are needed for participating. In thispaper we specify a formal model for folksonomies, briefly describeour own system BibSonomy, which allows for sharing both bookmarks andpublication references, and discuss first steps towards emergent semantics.Thu Feb 17 17:41:51 CET 2011Patras, GreeceProceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)JulyISBN 978-960-89282-6-839--43Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems20082008 itegpub methods_concepthierarchy methods_concepts myown ol_web2.0 olp3 semantic_relatedness tag_relatedness taggingsurvey tagorapub Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.Thu Feb 17 17:41:51 CET 2011HeidelbergThe Semantic Web -- ISWC 2008, Proc.Intl. Semantic Web Conference 2008615--631LNAISemantic Grounding of Tag Relatedness in Social Bookmarking Systems531820082008 grounding iswc2008 itegpub methods_concepthierarchy methods_concepts myown ol_web2.0 relatedness semantic semantic_relatedness similarity sw tag tagging taggingsurvey tagorapub Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For taskslike synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.Thu Feb 17 17:41:38 CET 2011Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sephttp://lwa07.informatik.uni-halle.de/kdml07/kdml07.htm109--112Position Paper: Ontology Learning from Folksonomies20072007 diploma_thesis ibm-kde-tagging itegpub methods_concepthierarchy myown ol_web2.0 ontology_learning rel1 rel2 taggingsurvey tagorapub The emergence of collaborative tagging systems with their underlying flat and uncontrolled resource organization paradigm has led to a large number of research activities focussing on a formal description and analysis of the resulting “folksonomies�?. An interesting outcome is that the characteristic qualities of these systems seem to be inverse to more traditional knowledge structuring approaches like taxonomies or ontologies: The latter provide rich and precise semantics, but suffer - amongst others - from a knowledge acquisition bottleneck. An important step towards exploiting the possible synergies by bridging the gap between both paradigms is the automatic extraction of relations between tags in a folksonomy. This position paper presents preliminary results of ongoing work to induce hierarchical relationships among tags by analyzing the aggregated data of collaborative tagging systems as a basis for an ontology learning procedure.Thu Feb 17 17:41:38 CET 2011Proceedings of the Dagstuhl Seminar on Social Web Communities08391Analyzing Tag Semantics Across Collaborative Tagging Systems20082008 dagstuhl iin2009 itegpub myown ol_web2.0 tag_semantics taggingsurvey tagorapub widely_related The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.Tue Feb 08 15:56:07 CET 2011SeptemberProceedings of the Dagstuhl Seminar on Social Web Communities20082008 communities dagstuhl social web tagorapub tagora itegpub Fri Jan 28 16:26:46 CET 2011Berlin, HeidelbergSocial Semantic Web18363--391X.media.pressSocial Bookmarking am Beispiel BibSonomy2009todelete! 2009 myown tagorapub itegpub BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitungder Universit{\"a}t Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalit{\"a}t, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.Fri Jan 28 11:33:49 CET 2011New York, NY, USAHT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia157--166Logsonomy - Social Information Retrieval with Logdata2008workshop tag_semantics iin2009 tagorapub Social bookmarking systems constitute an establishedpart of the Web 2.0. In such systemsusers describe bookmarks by keywordscalled tags. The structure behind these socialsystems, called folksonomies, can be viewedas a tripartite hypergraph of user, tag and resourcenodes. This underlying network showsspecific structural properties that explain itsgrowth and the possibility of serendipitousexploration.Today’s search engines represent the gatewayto retrieve information from the World WideWeb. Short queries typically consisting oftwo to three words describe a user’s informationneed. In response to the displayedresults of the search engine, users click onthe links of the result page as they expectthe answer to be of relevance.This clickdata can be represented as a folksonomyin which queries are descriptions ofclicked URLs. The resulting network structure,which we will term logsonomy is verysimilar to the one of folksonomies. In orderto find out about its properties, we analyzethe topological characteristics of the tripartitehypergraph of queries, users and bookmarkson a large snapshot of del.icio.us andon query logs of two large search engines.All of the three datasets show small worldproperties. The tagging behavior of users,which is explained by preferential attachmentof the tags in social bookmark systems, isreflected in the distribution of single querywords in search engines. We can concludethat the clicking behaviour of search engineusers based on the displayed search resultsand the tagging behaviour of social bookmarkingusers is driven by similar dynamics.Fri Jan 28 11:33:27 CET 2011Glasgow, UK30th European Conference on IR Research, ECIR 2008April101-113Lecture Notes in Computer ScienceA Comparison of Social Bookmarking with Traditional Search495620082008 nopaper bookmarking social logsonomies tagorapub itegpub Fri Jan 28 11:33:26 CET 2011AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering''4245-262Network Properties of Folksonomies2020072007 semantics emergent folksonomy tagorapub folksonomies l3s network itegpub fca Fri Jan 28 11:33:26 CET 2011Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the WebThe Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems20082008 systems bookmarking web tagger 2.0 itegpub social web2.0 folksonomy folksonomies tagorapub spam Fri Jan 28 11:33:25 CET 2011Banff, CanadaWorkshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007) at WWW 2007Organizing Publications and Bookmarks in BibSonomy2007challenge 2007 organizing BibSonomy bibsonomy tagorapub l3s itegpub Fri Jan 28 11:33:25 CET 2011AalborgProceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures87-102{BibSonomy}: A Social Bookmark and Publication Sharing System20062006 bookmarking social nepomuk bibsonomy OntologyHandbook FCA folksonomy tagorapub iccs l3s Social bookmark tools are rapidly emerging on the Web. In suchsystems users are setting up lightweight conceptual structurescalled folksonomies. The reason for their immediate success is thefact that no specific skills are needed for participating. In thispaper we specify a formal model for folksonomies and briefly describe our own system BibSonomy, which allows for sharing both bookmarksand publication references in a kind of personal library.Fri Jan 28 11:33:24 CET 2011Graz, Austria7th International Conference on Knowledge Management (I-KNOW '07)September356-364Conceptual Clustering of Social Bookmarking Sites2007sites 2007 tagging social folksonomy tagorapub folksonomies itegpub Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of thesystem, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.Fri Jan 28 11:33:21 CET 2011Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy -- A Search Engine Folksonomy20082008 search engine logsonomy logsonomies folksonomy tagorapub folksonomies itegpub In social bookmarking systems users describe bookmarksby keywords called tags. The structure behindthese social systems, called folksonomies, can beviewed as a tripartite hypergraph of user, tag and resourcenodes. This underlying network shows specificstructural properties that explain its growth and the possibilityof serendipitous exploration.Search engines filter the vast information of the web.Queries describe a user’s information need. In responseto the displayed results of the search engine, users clickon the links of the result page as they expect the answerto be of relevance. The clickdata can be represented as afolksonomy in which queries are descriptions of clickedURLs. This poster analyzes the topological characteristicsof the resulting tripartite hypergraph of queries,users and bookmarks of two query logs and compares ittwo a snapshot of the folksonomy del.icio.us.Fri Jan 28 11:33:17 CET 2011HeidelbergProc. First International Conference on Semantics And Digital Media Technology (SAMT) December56-70LNCSTrend Detection in Folksonomies43062006intranet 2006 trend pagerank hotho schmitz jaeschke l3s itegpub detection triadic stumme nepomuk folksonomy tagorapub folkrank UniK As the number of resources on the web exceeds by far the number ofdocuments one can track, it becomes increasingly difficult to remainup to date on ones own areas of interest. The problem becomes moresevere with the increasing fraction of multimedia data, from whichit is difficult to extract some conceptual description of theircontents.One way to overcome this problem are social bookmark tools, whichare rapidly emerging on the web. In such systems, users are settingup lightweight conceptual structures called folksonomies, andovercome thus the knowledge acquisition bottleneck. As more and morepeople participate in the effort, the use of a common vocabularybecomes more and more stable. We present an approach for discoveringtopic-specific trends within folksonomies. It is based on adifferential adaptation of the PageRank algorithm to the triadichypergraph structure of a folksonomy. The approach allows for anykind of data, as it does not rely on the internal structure of thedocuments. In particular, this allows to consider different datatypes in the same analysis step. We run experiments on a large-scalereal-world snapshot of a social bookmarking system.Tue Jun 29 22:44:43 CEST 2010AmsterdamAI Communications4231-247Tag Recommendations in Social Bookmarking Systems2120082.0 2008 Recommendations bookmarking itegpub logsonomies myown recommendations recommender social systems tag tagorapub tags web web2.0 web20 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.