%0 
%0 Conference Proceedings
%A Bullock, Beate Navarro; Lerch, Hana; Ro\ssnagel, Alexander; Hotho, Andreas & Stumme, Gerd
%D 2011
%T Privacy-aware spam detection in social bookmarking systems
%E 
%B Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies
%C New York, NY, USA
%I ACM
%V 
%6
%N 
%P 15:1--15:8
%& 
%Y 
%S i-KNOW '11
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%@ 978-1-4503-0732-1
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%1 
%2 Privacy-aware spam detection in social bookmarking systems
%3 inproceedings
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%# 
%$ 
%F bullock2011privacyaware
%K 2011, aware, classification, data-mining, detection, info20, itegpub, myown, social, spam, spam-detection, web2.0, web20
%X With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. However, for Web 2.0 service providers, the data provided by users is a valuable source for offering effective, personalised data mining services. One major application is the detection of spam in social bookmarking systems: in order to prevent a decrease of content quality, providers need to distinguish spammers and exclude them from the system. They thereby experience a conflict of interests: on the one hand, they need to identify spammers based on the information they collect about users, on the other hand, they need to respect privacy concerns and process as few personal data as possible. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored. In this paper we address these questions by presenting a data privacy aware feature engineering approach. It consists of the design of features for spam classification which are evaluated according to both, performance and privacy conditions. Experiments using data from the social bookmarking system BibSonomy show that both conditions must not exclude each other.
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%U http://doi.acm.org/10.1145/2024288.2024306
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%0 
%0 Journal Article
%A Hotho, Andreas; Benz, Dominik; Eisterlehner, Folke; J\"a,schke, Robert; Krause, Beate; Schmitz, Christoph & Stumme, Gerd
%D 2010
%T Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System fur Wissenschaftler
%E 
%B HMD -- Praxis der Wirtschaftsinformatik
%C 
%I 
%V Heft 271
%6
%N 
%P 47-58
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%Y 
%S 
%7
%8 February
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%@ 1436-3011
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%F hotho2010publikationsmanagement
%K 2.0, 2010, info20, itegpub, l3s, management, myown, paper, semantic, tagging, web, web2.0
%X Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer gr{\"o}{\ss}erer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenw{\"a}rtigkeit, die st{\"a}ndige Verf{\"u}gbarkeit, aber auch die M{\"o}glichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gr{\"u}nde f{\"u}r ihren gegenw{\"a}rtigen Erfolg. Der Artikel f{\"u}hrt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabl{\"a}ufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schlie{\ss}t mit Querbez{\"u}gen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.
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%U 
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%0 
%0 Journal Article
%A Krause, Beate; Lerch, Hana; Hotho, Andreas; Roßnagel, Alexander & Stumme, Gerd
%D 2010
%T Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy
%E 
%B Informatik-Spektrum
%C 
%I Springer
%V 
%6
%N 
%P 1-12
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 0170-6012
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%1 
%2 SpringerLink - Informatik-Spektrum, Online First™
%3 article
%4 
%# 
%$ 
%F springerlink:10.1007/s00287-010-0485-8
%K 2010, bibsonomy, datenschutz, info2.0, info20, itegpub, l3s, myown, privacy, web2.0
%X Soziale Tagging-Systeme gehören zu den in den vergangenen Jahren entstandenen Web2.0-Systemen. Sie ermöglichen es Anwendern, beliebige Informationen in das Internet einzustellen und untereinander auszutauschen. Je nach Anbieter verlinken Nutzer Videos, Fotos oder Webseiten und beschreiben die eingestellten Medien mit entsprechenden Schlagwörtern (Tags). Die damit einhergehende freiwillige Preisgabe oftmals persönlicher Informationen wirft Fragen im Bereich der informationellen Selbstbestimmung auf. Dieses Grundrecht gewährleistet dem Einzelnen, grundsätzlich selbst über die Preisgabe und Verwendung seiner persönlichen Daten zu bestimmen. Für viele Funktionalitäten, wie beispielsweise Empfehlungsdienste oder die Bereitstellung einer API, ist eine solche Kontrolle allerdings schwierig zu gestalten. Oftmals existieren keine Richtlinien, inwieweit Dienstanbieter und weitere Dritte diese öffentlichen Daten (und weitere Daten, die bei der Nutzung des Systems anfallen) nutzen dürfen. Dieser Artikel diskutiert anhand eines konkreten Systems typische, für den Datenschutz relevante Funktionalitäten und gibt Handlungsanweisungen für eine datenschutzkonforme technische Gestaltung.
%Z 
%U http://dx.doi.org/10.1007/s00287-010-0485-8
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%0 
%0 Book
%A ?
%D 2009 
%T Knowledge Discovery Enhanced with Semantic and Social Information
%E 
%B 
%C Heidelberg
%I Springer
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%F berendt09knowledge
%K 2.0, discovery, information, knowledge, semantic, social, web, web2.0
%X 
%Z (to appear)
%U 
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%^

%0 
%0 Conference Proceedings
%A Cattuto, Ciro; Benz, Dominik; Hotho, Andreas & Stumme, Gerd
%D 2008
%T Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems
%E 
%B Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)
%C Patras, Greece
%I 
%V 
%6
%N 
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%& 
%Y 
%S 
%7
%8 July
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%1 
%2 Publications of Gerd Stumme
%3 inproceedings
%4 
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%$ 
%F cattuto08-semantic
%K 2.0, 2008, collaborative, folksonomies, folksonomy, itegpub, myown, semantic, systems, tagging, web, web2.0
%X 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.
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%U http://olp.dfki.de/olp3/
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%0 
%0 Journal Article
%A Jäschke, Robert; Marinho, Leandro; Hotho, Andreas; Schmidt-Thieme, Lars & Stumme, Gerd
%D 2008
%T Tag Recommendations in Social Bookmarking Systems
%E 
%B AI Communications
%C 
%I IOS Press
%V 21
%6
%N 4
%P 231-247
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%Y 
%S 
%7
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%? 
%! 
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%@ 0921-7126
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%1 
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%3 article
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%F jaeschke2008tag
%K 2.0, 2008, Recommendations, bookmarking, itegpub, logsonomies, myown, recommendations, recommender, social, systems, tag, tagorapub, tags, web, web2.0, web20
%X 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.

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%U http://dx.doi.org/10.3233/AIC-2008-0438
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%^

%0 
%0 Conference Proceedings
%A Krause, Beate; Jäschke, Robert; Hotho, Andreas & Stumme, Gerd
%D 2008
%T Logsonomy - Social Information Retrieval with Logdata
%E 
%B HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia
%C New York, NY, USA
%I ACM
%V 
%6
%N 
%P 157--166
%& 
%Y 
%S 
%7
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%@ 978-1-59593-985-2
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%3 inproceedings
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%F krause2008logsonomy
%K 2.0, 2008, analysis, folksonomy, information, itegpub, logsonomy, myown, network, retrieval, search, social, tagorapub, web, web2.0, web20
%X Social bookmarking systems constitute an established
part of the Web 2.0. In such systems
users describe bookmarks by keywords
called tags. The structure behind these social
systems, called folksonomies, can be viewed
as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows
specific structural properties that explain its
growth and the possibility of serendipitous
exploration.
Today’s search engines represent the gateway
to retrieve information from the World Wide
Web. Short queries typically consisting of
two to three words describe a user’s information
need. In response to the displayed
results of the search engine, users click on
the links of the result page as they expect
the answer to be of relevance.
This clickdata can be represented as a folksonomy
in which queries are descriptions of
clicked URLs. The resulting network structure,
which we will term logsonomy is very
similar to the one of folksonomies. In order
to find out about its properties, we analyze
the topological characteristics of the tripartite
hypergraph of queries, users and bookmarks
on a large snapshot of del.icio.us and
on query logs of two large search engines.
All of the three datasets show small world
properties. The tagging behavior of users,
which is explained by preferential attachment
of the tags in social bookmark systems, is
reflected in the distribution of single query
words in search engines. We can conclude
that the clicking behaviour of search engine
users based on the displayed search results
and the tagging behaviour of social bookmarking
users is driven by similar dynamics.
%Z 
%U http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia
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%0 
%0 Conference Proceedings
%A Krause, Beate; Schmitz, Christoph; Hotho, Andreas & Stumme, Gerd
%D 2008
%T The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems
%E 
%B Proc. of the Fourth International Workshop on  Adversarial Information Retrieval on the Web
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%2 Publications of Gerd Stumme
%3 inproceedings
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%# 
%$ 
%F krause2008antisocial
%K 2.0, 2008, bookmarking, folksonomies, folksonomy, itegpub, myown, social, spam, systems, tagger, tagorapub, web, web2.0
%X 
%Z 
%U http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf
%+
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%0 
%0 Conference Proceedings
%A Aroyo, Lora; Denaux, Ronald; Dimitrova, Vania & Pye, Michael
%D 2006
%T Interactive Ontology-Based User Knowledge Acquisition: A Case Study.
%E 
%B ESWC
%C 
%I 
%V 
%6
%N 
%P 560-574
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%2 DBLP Record 'conf/esws/AroyoDDP06'
%3 inproceedings
%4 DBLP:conf/esws/2006
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%$ 
%F DBLP:conf/esws/AroyoDDP06
%K proceedings, 2006, web2.0, and, personalisation, eswc, european, conference, semantic, mining
%X 
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%0 
%0 Conference Proceedings
%A Millen, David R.; Feinberg, Jonathan & Kerr, Bernard
%D 2006
%T Dogear: Social bookmarking in the enterprise
%E 
%B CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems
%C New York, NY, USA
%I ACM Press
%V 
%6
%N 
%P 111--120
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%@ 1-59593-372-7
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%3 inproceedings
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%F millen06dogear
%K Social, bookmarking, dogear, intranet, km, knowledge, management, web2.0, wissensmanagement, wm
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%0 
%0 Conference Proceedings
%A Mika, Peter
%D 2005
%T Ontologies Are Us: A Unified Model of Social Networks and Semantics
%E 
%B International Semantic Web Conference
%C 
%I Springer
%V 
%6
%N 
%P 522-536
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%Y 
%S LNCS
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%2 http://www.cs.vu.nl/~pmika/research/papers/ISWC-folksonomy.pdf
%3 inproceedings
%4 
%# mlux
%$ 
%F Mika2005
%K web2.0, folksonomies, tagging, socialnetworks, socialsoftware, seminar2006
%X 
%Z 
%U http://dx.doi.org/10.1007/11574620_38
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