@article{mitzlaff2014social,
  abstract = {Applications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the },
  author = {Mitzlaff, Folke and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd},
  doi = {10.1007/s13278-014-0216-2},
  eid = {216},
  interhash = {7e02f08a123c801c33ac93109394adfb},
  intrahash = {5b268a7c5308af783c3028573ffcd0c0},
  issn = {1869-5450},
  journal = {Social Network Analysis and Mining},
  language = {English},
  number = 1,
  publisher = {Springer Vienna},
  title = {The social distributional hypothesis: a pragmatic proxy for homophily in online social networks},
  url = {http://dx.doi.org/10.1007/s13278-014-0216-2},
  volume = 4,
  year = 2014
}

@article{mueller-2014b,
  abstract = {The combination of ubiquitous and social computing is an emerging
 research area which integrates different but complementary methods,
 techniques and tools. In this paper, we focus on the Ubicon platform,
 its applications, and a large spectrum of analysis results.
 
 
 Ubicon provides an extensible framework for building and hosting applications
 targeting both ubiquitous and social environments. We summarize the
 architecture and exemplify its implementation using four real-world
 applications built on top of Ubicon. In addition, we discuss several
 scientific experiments in the context of these applications in order
 to give a better picture of the potential of the framework, and discuss
 analysis results using several real-world data sets collected utilizing
 Ubicon.},
  author = {Atzmueller, Martin and Becker, Martin and Kibanov, Mark and Scholz, Christoph and Doerfel, Stephan and Hotho, Andreas and Macek, Bjoern-Elmar and Mitzlaff, Folke and Mueller, Juergen and Stumme, Gerd},
  doi = {10.1080/13614568.2013.873488},
  interhash = {6364e034fa868644b30618dc887c0270},
  intrahash = {d38f1e01e735253b4cad2c98c1027659},
  issn = {1361-4568},
  journal = {New Review of Hypermedia and Multimedia},
  month = {#mar#},
  number = 20,
  pages = {53--77},
  title = {Ubicon and its Applications for Ubiquitous Social Computing},
  url = {http://dx.doi.org/10.1080/13614568.2013.873488},
  volume = 1,
  year = 2014
}

@incollection{mitzlaff2013semantics,
  address = {Heidelberg, Germany},
  author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Complex Networks IV},
  doi = {10.1007/978-3-642-36844-8_2},
  editor = {Ghoshal, Gourab and Poncela-Casasnovas, Julia and Tolksdorf, Robert},
  interhash = {bf333426bb7af5f01bf0c465c1bfe1fc},
  intrahash = {0a35f1ed66fcd342a6a44d70c63fb735},
  optisbn = {978-3-642-36843-1},
  opturl = {http://dx.doi.org/10.1007/978-3-642-36844-8_2},
  publisher = {Springer Verlag},
  series = {Studies in Computational Intelligence},
  title = {{Semantics of User Interaction in Social Media}},
  volume = 476,
  year = 2013
}

@inproceedings{atzmueller2013towards,
  address = {New York, NY, USA},
  author = {Atzmueller, Martin and Hilgenberg, Katy},
  booktitle = {Proc. 4th International Workshop on Modeling Social Media (MSM 2013), Hypertext 2013},
  interhash = {b0d93d41ff9e84514d614cd2b3507a1d},
  intrahash = {4ebea4979524a9c1c0d41845e41e33a9},
  publisher = {ACM Press},
  title = {{Towards Capturing Social Interactions with SDCF: An Extensible Framework for Mobile Sensing and Ubiquitous Data Collection}},
  year = 2013
}

@inproceedings{scholz2013people,
  author = {Scholz, Christoph and Atzmueller, Martin and Kibanov, Mark and Stumme, Gerd},
  booktitle = {ASONAM},
  interhash = {8b6051b794789000c4baa5ab059fab18},
  intrahash = {bf958ff2b11df1d9d15d9287ea07a5c9},
  title = {How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks},
  year = 2013
}

@article{atzmueller2013exploratory,
  author = {Atzmueller, Martin and Lemmerich, Florian},
  interhash = {6e83bf4017fffe31a5632289d91c1b6d},
  intrahash = {9f176520035c05191d77ebd53803b817},
  journal = {International Journal of Web Science (Special Issue on Social Web Search and Mining)},
  number = {1/2},
  title = {{Exploratory Pattern Mining on Social Media using Geo-References and Social Tagging Information}},
  volume = 2,
  year = 2013
}

@article{schulz2013rechtliche,
  author = {Schulz, Thomas and Skistims, Hendrik and Zirfas, Julia and Atzmueller, Martin and Scholz, Christoph},
  interhash = {224ac0c2ca760bea1ccdb5ee66959a8b},
  intrahash = {2ec777edc0adc9f74292caf4ae9ad4f0},
  journal = {ZD},
  pages = {60--65},
  title = {{Rechtliche Ausgestaltung sozialer Konferenzplattformen}},
  volume = 2,
  year = 2013
}

@inproceedings{mitzlaff2011semantics,
  address = {Bamberg, Germany},
  author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Proc. LWA 2013 (KDML Special Track)},
  interhash = {73088600a500f7d06768615d6e1c2b3d},
  intrahash = {820ffb2166b330bf60bb30b16e426553},
  publisher = {University of Bamberg},
  title = {{On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)}},
  year = 2011
}

@misc{mitzlaff2013userrelatedness,
  abstract = {With social media and the according social and ubiquitous applications
finding their way into everyday life, there is a rapidly growing amount of user
generated content yielding explicit and implicit network structures. We
consider social activities and phenomena as proxies for user relatedness. Such
activities are represented in so-called social interaction networks or evidence
networks, with different degrees of explicitness. We focus on evidence networks
containing relations on users, which are represented by connections between
individual nodes. Explicit interaction networks are then created by specific
user actions, for example, when building a friend network. On the other hand,
more implicit networks capture user traces or evidences of user actions as
observed in Web portals, blogs, resource sharing systems, and many other social
services. These implicit networks can be applied for a broad range of analysis
methods instead of using expensive gold-standard information.
  In this paper, we analyze different properties of a set of networks in social
media. We show that there are dependencies and correlations between the
networks. These allow for drawing reciprocal conclusions concerning pairs of
networks, based on the assessment of structural correlations and ranking
interchangeability. Additionally, we show how these inter-network correlations
can be used for assessing the results of structural analysis techniques, e.g.,
community mining methods.},
  author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  interhash = {40aa075d925f2e6e009986fd9e60b11b},
  intrahash = {cbed5fadde51ddb20c6a470ced93556a},
  note = {cite arxiv:1309.3888},
  title = {User-Relatedness and Community Structure in Social Interaction Networks},
  url = {http://arxiv.org/abs/1309.3888},
  year = 2013
}

@inproceedings{kibanov2013evolution,
  address = {Boston, MA, USA},
  author = {Kibanov, Mark and Atzmueller, Martin and Scholz, Christoph and Stumme, Gerd},
  booktitle = {Proc. IEEE CPSCom 2013},
  interhash = {14e73d96c8554e73214c36b49add934c},
  intrahash = {5824b6151b2046d6295e4300311b7e8e},
  publisher = {IEEE Computer Society},
  title = {{On the Evolution of Contacts and Communities in Networks of Face-to-Face Proximity}},
  year = 2013
}

@article{mitzlaff2013userrelatedness,
  author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  interhash = {40aa075d925f2e6e009986fd9e60b11b},
  intrahash = {424d0f2d4a5c9a0eb68cbf2fc5b0010a},
  journal = {CoRR/abs},
  title = {{User-Relatedness and Community Structure in Social Interaction Networks}},
  volume = {1309.3888},
  year = 2013
}

@article{landia2013deeper,
  abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.},
  author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan},
  interhash = {e8095b13630452ce3ecbae582f32f4bc},
  intrahash = {e585a92994be476480545eb62d741642},
  journal = {cs.IR},
  title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
  url = {http://arxiv.org/abs/1310.1498},
  volume = {1310.1498},
  year = 2013
}

@inproceedings{doerfel2013analysis,
  abstract = {Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.},
  acmid = {2507222},
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Jäschke, Robert},
  booktitle = {Proceedings of the 7th ACM conference on Recommender systems},
  doi = {10.1145/2507157.2507222},
  interhash = {3eaf2beb1cdad39b7c5735a82c3338dd},
  intrahash = {aa4b3d79a362d7415aaa77625b590dfa},
  isbn = {978-1-4503-2409-0},
  location = {Hong Kong, China},
  numpages = {4},
  pages = {343--346},
  publisher = {ACM},
  series = {RecSys '13},
  title = {An analysis of tag-recommender evaluation procedures},
  url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf},
  year = 2013
}

@inproceedings{kibanov2013evolution,
  address = {Bamberg, Germany},
  author = {Kibanov, Mark and Atzmueller, Martin and Scholz, Christoph and Stumme, Gerd},
  booktitle = {Proc. LWA 2013 (KDML Special Track)},
  interhash = {84a970ee998f7e8d24b597c285e1887c},
  intrahash = {a5d94c9930fd413a404efe6d9420a752},
  publisher = {University of Bamberg},
  title = {{Evolution of Contacts and Communities in Networks of Face-to-Face Proximity (Extended Abstract, Resubmission)}},
  year = 2013
}

@misc{atzmueller2013conferator,
  author = {Atzmueller, Martin and Kibanov, Mark and Scholz, Christoph and Stumme, Gerd},
  howpublished = {Poster at INFORMATIK 2013},
  institution = {University of Koblenz-Landau},
  interhash = {caba49a0a623346dad82849aba632778},
  intrahash = {2668432d7c035c34c4af4f6f96e22397},
  publisher = {INFORMATIK 2013},
  title = {{Conferator - a Social System for Conference and Contact Management}},
  year = 2013
}

@incollection{atzmueller2013social,
  address = {Heidelberg, Germany},
  author = {Atzmueller, Martin},
  booktitle = {Mobile Social Networking: An Innovative Approach},
  editor = {Chin, Alvin and Zhang, Daqing},
  interhash = {1be75b604acbaf39653eeca9833782df},
  intrahash = {cd910f3a16c9368e7b73407708452653},
  publisher = {Springer Verlag},
  title = {{Social Behavior in Mobile Social Networks: Characterizing Links, Roles and Communities}},
  year = 2013
}

@book{rothberghofer2013proceedings,
  address = {Annecy, France},
  editor = {Roth-Berghofer, Thomas and Oussena, Samia and Atzmueller, Martin},
  interhash = {f1f8980780e57fc36c9c44ce7d3673b4},
  intrahash = {2b36fe460601217691180f0affbbe286},
  publisher = {CONTEXT 2013},
  title = {{Proceedings of the 2013 International Smart University Workshop (SmartU 2013)}},
  year = 2013
}

@article{Atzmueller:12,
  author = {Atzmueller, Martin},
  interhash = {ce21e72d189207dbee58420af81efca8},
  intrahash = {a66dc503e2f90d0a484e0dbef5febcd3},
  journal = {Informatik Spektrum},
  number = 2,
  pages = {132-135},
  title = {Mining Social Media},
  volume = 35,
  year = 2012
}

@incollection{LA:12,
  address = {Heidelberg, Germany},
  alteditor = {Editor},
  author = {Lemmerich, Florian and Atzmueller, Martin},
  booktitle = {{Modeling and Mining Ubiquitous Social Media}},
  interhash = {ae9dc5bfe7f42f1e9ca59aadda4bfd9e},
  intrahash = {8435f029bf0e32340e2cf44d0eeb65e7},
  publisher = {Springer Verlag},
  series = {LNAI},
  title = {{Describing Locations using Tags and Images: Explorative Pattern Mining in Social Media}},
  url = {http://www.kde.cs.uni-kassel.de/atzmueller/paper/lemmerich-explorative-pattern-mining-socia-media-lnai-2012.pdf},
  volume = 7472,
  year = 2012
}

@proceedings{CAH:12,
  address = {New York, NY, USA},
  editor = {Chin, Alvin and Atzmueller, Martin and Helic, Denis},
  interhash = {8c21f6c9eb9d4658ca2374f12a63e950},
  intrahash = {338ca2515a0f6cd77996d1bbf54c7f4d},
  publisher = {ACM Press},
  title = {{Proceedings MSM 2012: Workshop on Modeling Social Media -- Collective Intelligence in Social Media}},
  year = 2012
}

@article{Atzmueller:12c,
  author = {Atzmueller, Martin},
  interhash = {0b20c1d53d5df05326d594726273c2fb},
  intrahash = {7b616e64994893a2aad95b5ad95db662},
  journal = {WIREs: Data Mining and Knowledge Discovery},
  title = {{Mining Social Media: Key Players, Sentiments, and Communities}},
  volume = {In Press},
  year = 2012
}

@book{ACHH:12,
  address = {Heidelberg, Germany},
  editor = {Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas},
  interhash = {ebf8e8b66c6c0723092e11e40998d61f},
  intrahash = {a0e5d144b39199fa4acb6319f29e7a15},
  publisher = {Springer Verlag},
  series = {Lecture Notes in Computer Science},
  title = {Modeling and Mining Ubiquitous Social Media},
  url = {http://www.springer.com/computer/ai/book/978-3-642-33683-6},
  volume = 7472,
  year = 2012
}

@inproceedings{mueller-2012,
  abstract = {The connection of ubiquitous and social computing is an emerging research
 area which is combining two prominent areas of computer science.
 In this paper, we tackle this topic from different angles: We describe
 data mining methods for ubiquitous and social data, specifically
 focusing on physical and social activities, and provide exemplary
 analysis results. Furthermore, we give an overview on the Ubicon
 platform which provides a framework for the creation and hosting
 of ubiquitous and social applications for diverse tasks and projects.
 Ubicon features the collection and analysis of both physical and
 social activities of users for enabling inter-connected applications
 in ubiquitous and social contexts. We summarize three real-world
 systems built on top of Ubicon, and exemplarily discuss the according
 mining and analysis aspects.},
  address = {Washington, DC, USA},
  author = {Atzmueller, Martin and Becker, Martin and Doerfel, Stephan and Kibanov, Mark and Hotho, Andreas and Macek, Björn-Elmar and Mitzlaff, Folke and Mueller, Juergen and Scholz, Christoph and Stumme, Gerd},
  booktitle = {IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012, Besançon, France, 20-23 November, 2012},
  interhash = {a2695fd9fe6e76b252edbd42d72b34ad},
  intrahash = {90847b1d969ac1ed1f4c8d7146416619},
  publisher = {IEEE},
  title = {Ubicon: Observing Social and Physical Activities},
  year = 2012
}

@book{balbymarinho2012recommender,
  abstract = {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.},
  author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
  doi = {10.1007/978-1-4614-1894-8},
  interhash = {0bb7f0588cd690d67cc73e219a3a24fa},
  intrahash = {87d6883ebd98e8810be45d7e7e4ade96},
  isbn = {978-1-4614-1893-1},
  month = feb,
  publisher = {Springer},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  title = {Recommender Systems for Social Tagging Systems},
  url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
  year = 2012
}

@inproceedings{SAS:12,
  address = {Boston, MA, USA},
  author = {Scholz, Christoph and Atzmueller, Martin and Stumme, Gerd},
  booktitle = {Proc. Fourth ASE/IEEE International Conference on Social Computing (SocialCom)},
  interhash = {9bc5d42018dbe8b926be214190258b3c},
  intrahash = {be5ae4b92170e7c595f5fdcac15b4786},
  publisher = {IEEE Computer Society},
  title = {{On the Predictability of Human Contacts: Influence Factors and the Strength of Stronger Ties}},
  url = {http://www.kde.cs.uni-kassel.de/atzmueller/paper/scholz-on-f2f-predictability-socialcom-2012.pdf},
  year = 2012
}

@inproceedings{doerfel2012leveraging,
  abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.},
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},
  doi = {10.1145/2365934.2365937},
  interhash = {beb2c81daf975eeed6e01e1b412196b1},
  intrahash = {64bf590675a833770b7d284871435a8d},
  isbn = {978-1-4503-1638-5},
  location = {Dublin, Ireland},
  month = sep,
  pages = {9--16},
  publisher = {ACM},
  title = {Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation },
  url = {http://doi.acm.org/10.1145/2365934.2365937},
  year = 2012
}

@incollection{ADHMS:12,
  address = {Heidelberg, Germany},
  alteditor = {Editor},
  author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {{Modeling and Mining Ubiquitous Social Media}},
  interhash = {4f1f4b515b01cc448a91b3e368deabad},
  intrahash = {d81d6f6ccdf3ff6572898d39c90e6354},
  publisher = {Springer Verlag},
  series = {LNAI},
  title = {Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles},
  url = {http://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-face-to-face-contacts-dynamics-lnai-2012.pdf},
  volume = 7472,
  year = 2012
}

@article{palla2007quantifying,
  author = {Palla, Gergely and Barabási, Albert-lászló and Vicsek, Tamás and Hungary, Budapest},
  interhash = {911359764769215213b2d31dc6394078},
  intrahash = {9c5a50822cb5580b66a7371b833bb2fe},
  pages = 2007,
  title = {Quantifying social group evolution},
  url = {http://130.203.133.150/viewdoc/summary?doi=10.1.1.119.7541},
  volume = 446,
  year = 2007
}

@article{goldbergtracking,
  author = {Goldberg, Mark and Magdon ismail, Malik and Nambirajan, Srinivas and Thompson, James},
  interhash = {cfde2673d41ea69c4ec2750749f76c8e},
  intrahash = {17a06b71460472ba0212a03debbe6a42},
  title = {Tracking and Predicting Evolution of Social Communities},
  url = {http://130.203.133.150/viewdoc/summary;jsessionid=F5AEFEF80D05D6B5A0262BA46AEE899D?doi=10.1.1.225.2523},
  year = {?}
}

@inproceedings{hristova2012mapping,
  abstract = {Communities of people are better mappers if they are spatially
clustered, as revealed in an interesting new paper by Hristova,
Mashhadi, Quattrone and Capra from UCL. "This preliminary analysis
inspires further inquiry because it shows a clear correlation between
spatial affiliation, the internal community structure and the
community’s engagement in terms of coverage", according to the
authors. They have studied the similarity patterns among eight hundred
contributors to OpenStreetMap, the well-known crowdmapping project and
detected the hidden community structure. It is a very promising field
of research, coupling a social network analysis of crowdsourced data.
Participants to such projects are rarely independent individuals: in
most cases, they involve communities more than single participants and
it would be crucial to uncover how the underlying social structure
reflects on the quantity and the quality of the collected data. It has
the greatest relevance for citizen science projects, as data quality
is often the key issue determining the success or the failure of the
collective effort. },
  author = {Hristova, Desislava and Mashhadi, Afra and Quattrone, Giovanni and Capra, Licia},
  booktitle = {Proc. When the City Meets the Citizen Workshop (WCMCW)},
  interhash = {373e02fe56d30b26261a33135e0b7a45},
  intrahash = {f0a69ac56b94a471b470ebd56545fafd},
  month = jun,
  title = {Mapping Community Engagement with Urban Crowd-Sourcing},
  url = {http://www.cs.ucl.ac.uk/staff/l.capra/publications/wcmcw12.pdf},
  year = 2012
}

@incollection{jaeschke2012challenges,
  abstract = {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.},
  address = {Berlin/Heidelberg},
  affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany},
  author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {Recommender Systems for the Social Web},
  doi = {10.1007/978-3-642-25694-3_3},
  editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.},
  interhash = {75b1a6f54ef54d0126d0616b5bf77563},
  intrahash = {7d41d332cccc3e7ba8e7dadfb7996337},
  isbn = {978-3-642-25694-3},
  pages = {65--87},
  publisher = {Springer},
  series = {Intelligent Systems Reference Library},
  title = {Challenges in Tag Recommendations for Collaborative Tagging Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3},
  volume = 32,
  year = 2012
}

@inproceedings{benz2011measuring,
  abstract = {Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.},
  address = {Heraklion, Crete},
  author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus},
  booktitle = {Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011)},
  editor = {Antoniou, Grigoris and Grobelnik, Marko and Simperl, Elena and Parsia, Bijan and Plexousakis, Dimitris and Pan, Jeff and Leenheer, Pieter De},
  interhash = {33a2078f3836293d71c449d5376fc440},
  intrahash = {b245d492f1f9fa41b62b79b6dec77241},
  month = may,
  title = {One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata},
  url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf},
  year = 2011
}

@inproceedings{benz2011measuring,
  author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus},
  booktitle = {Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation},
  interhash = {33a2078f3836293d71c449d5376fc440},
  intrahash = {923d369285422c758398cbe92e3532cd},
  title = {One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata},
  year = 2011
}

@inproceedings{bullock2011privacyaware,
  abstract = {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.},
  acmid = {2024306},
  address = {New York, NY, USA},
  articleno = {15},
  author = {Bullock, Beate Navarro and Lerch, Hana and Ro\ssnagel, Alexander and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies},
  doi = {10.1145/2024288.2024306},
  interhash = {7a2d6a35c124ea0fe31c962f8f150916},
  intrahash = {00a8f31185a34957eb16d500d7d51398},
  isbn = {978-1-4503-0732-1},
  location = {Graz, Austria},
  numpages = {8},
  pages = {15:1--15:8},
  publisher = {ACM},
  series = {i-KNOW '11},
  title = {Privacy-aware spam detection in social bookmarking systems},
  url = {http://doi.acm.org/10.1145/2024288.2024306},
  year = 2011
}

@incollection{marinho2011social,
  abstract = {The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. 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 STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.},
  address = {New York},
  author = {Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis},
  booktitle = {Recommender Systems Handbook},
  doi = {10.1007/978-0-387-85820-3_19},
  editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.},
  interhash = {2d4afa6f7fb103ccc166c9c5d629cdd1},
  intrahash = {708be7b5c269bd3a9d3d2334f858d52d},
  isbn = {978-0-387-85820-3},
  pages = {615--644},
  publisher = {Springer},
  title = {Social Tagging Recommender Systems},
  url = {http://dx.doi.org/10.1007/978-0-387-85820-3_19},
  year = 2011
}

@inproceedings{BAKHSS:11,
  address = {Chemnitz},
  author = {Behrenbruch, Kay and Atzmueller, Martin and Kniewel, Romy and Hoberg, Sebastian and Stumme, Gerd and Schmidt, Ludger},
  booktitle = {GfA-Frühjahrskongress},
  interhash = {bb1435b451f54abf143ea892375abf55},
  intrahash = {addbaaba7aec8360e23284c849e216ad},
  title = {Gestaltung technisch-sozialer Vernetzung in der Arbeitsorganisation: Untersuchung zur Nutzerakzeptanz von RFID-Technologie},
  year = 2011
}

@inproceedings{Backstrom:2007:WAT:1242572.1242598,
  abstract = {In a social network, nodes correspond topeople or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes.},
  acmid = {1242598},
  address = {New York, NY, USA},
  author = {Backstrom, Lars and Dwork, Cynthia and Kleinberg, Jon},
  booktitle = {Proceedings of the 16th international conference on World Wide Web},
  doi = {10.1145/1242572.1242598},
  interhash = {aa7d0f96c372d2c03d228f27a7f4b66b},
  intrahash = {913059fcbf0453c60ff8b79e2705742c},
  isbn = {978-1-59593-654-7},
  location = {Banff, Alberta, Canada},
  numpages = {10},
  pages = {181--190},
  publisher = {ACM},
  series = {WWW '07},
  title = {Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography},
  url = {http://doi.acm.org/10.1145/1242572.1242598},
  year = 2007
}

@misc{Narayanan2009,
  abstract = {  Operators of online social networks are increasingly sharing potentially
sensitive information about users and their relationships with advertisers,
application developers, and data-mining researchers. Privacy is typically
protected by anonymization, i.e., removing names, addresses, etc.
  We present a framework for analyzing privacy and anonymity in social networks
and develop a new re-identification algorithm targeting anonymized
social-network graphs. To demonstrate its effectiveness on real-world networks,
we show that a third of the users who can be verified to have accounts on both
Twitter, a popular microblogging service, and Flickr, an online photo-sharing
site, can be re-identified in the anonymous Twitter graph with only a 12% error
rate.
  Our de-anonymization algorithm is based purely on the network topology, does
not require creation of a large number of dummy "sybil" nodes, is robust to
noise and all existing defenses, and works even when the overlap between the
target network and the adversary's auxiliary information is small.
},
  author = {Narayanan, Arvind and Shmatikov, Vitaly},
  interhash = {ffb21f5ed2b9b879d911d0b68f3d5c07},
  intrahash = {396299d0adaba60baa0f4c2bd28a93b8},
  note = {cite arxiv:0903.3276
Comment: Published in the 30th IEEE Symposium on Security and Privacy, 2009.
  The definitive version is available at:
  http://www.cs.utexas.edu/~shmat/shmat_oak09.pdf Frequently Asked Questions
  are answered at: http://www.cs.utexas.edu/~shmat/socialnetworks-faq.html},
  title = {De-anonymizing Social Networks},
  url = {http://arxiv.org/abs/0903.3276},
  year = 2009
}

@inproceedings{bozsak2002towards,
  author = {Bozsak, E. and Ehrig, Marc and Handschuh, Siegfried and Hotho, Andreas and Maedche, Alexander and Motik, Boris and Oberle, Daniel and Schmitz, Christoph and Staab, Steffen and Stojanovic, Ljiljana and Stojanovic, Nenad and Studer, Rudi and Stumme, Gerd and Sure, York and Tane, Julien and Volz, Raphael and Zacharias, Valentin},
  booktitle = {Proceedings of the Third International Conference on E-Commerce and Web Technologies (EC-Web 2002), Aix-en-Provence, France},
  editor = {Bauknecht, Kurt and Tjoa, A. Min and Quirchmayr, Gerald},
  interhash = {940750309ac472ea48a712e16b5d902a},
  intrahash = {d0aa1d2d01e378046e1693babc026836},
  pages = {304-313},
  publisher = {Springer},
  series = {LNCS},
  title = {KAON - Towards a large scale Semantic Web},
  url = {http://www.aifb.uni-karlsruhe.de/WBS/ysu/publications/2002_ecweb_kaon.pdf},
  volume = 2455,
  year = 2002
}

@inproceedings{koerner2010thinking,
  abstract = {Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence  of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.},
  address = {Raleigh, NC, USA},
  author = {Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 19th International World Wide Web Conference (WWW 2010)},
  interhash = {5afe6e4ce8357d8ac9698060fb438468},
  intrahash = {45f8d8f2a8251a5e988c596a5ebb3f2d},
  month = apr,
  publisher = {ACM},
  title = {Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity},
  url = {http://www.kde.cs.uni-kassel.de/benz/papers/2010/koerner2010thinking.pdf},
  year = 2010
}

@article{berendt2010bridging,
  author = {Berendt, Bettina and Hotho, Andreas and Stumme, Gerd},
  doi = {DOI: 10.1016/j.websem.2010.04.008},
  interhash = {4969eb2b7bf1fabe60c5f23ab6383d77},
  intrahash = {f8d7bc2af5753906dc3897196daac18c},
  issn = {1570-8268},
  journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
  note = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences},
  number = {2-3},
  pages = {95 - 96},
  title = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0},
  url = {http://www.sciencedirect.com/science/article/B758F-4YXK4HW-1/2/4cb514565477c54160b5e6eb716c32d7},
  volume = 8,
  year = 2010
}

@article{benz2010social,
  abstract = {Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.},
  address = {Berlin / Heidelberg},
  author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Mitzlaff, Folke and Schmitz, Christoph and Stumme, Gerd},
  doi = {10.1007/s00778-010-0208-4},
  interhash = {57fe43734b18909a24bf5bf6608d2a09},
  intrahash = {5d9541d5e8470a1867d995d3e0514697},
  issn = {1066-8888},
  journal = {The VLDB Journal},
  publisher = {Springer},
  title = {The social bookmark and publication management system BibSonomy},
  url = {http://dx.doi.org/10.1007/s00778-010-0208-4},
  year = 2010
}

@article{jaeschke2008tag,
  abstract = {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.
},
  address = {Amsterdam},
  author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
  doi = {10.3233/AIC-2008-0438},
  editor = {Giunchiglia, Enrico},
  interhash = {b2f1aba6829affc85d852ea93a8e39f7},
  intrahash = {955bcf14f3272ba6eaf3dadbef6c0b10},
  issn = {0921-7126},
  journal = {AI Communications},
  number = 4,
  pages = {231-247},
  publisher = {IOS Press},
  title = {Tag Recommendations in Social Bookmarking Systems},
  url = {http://dx.doi.org/10.3233/AIC-2008-0438},
  vgwort = {63},
  volume = 21,
  year = 2008
}

@inproceedings{krause2008logsonomy,
  abstract = {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.},
  address = {New York, NY, USA},
  author = {Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
  booktitle = {HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia},
  doi = {http://doi.acm.org/10.1145/1379092.1379123},
  interhash = {6d34ea1823d95b9dbf37d4db4d125d2a},
  intrahash = {e64d14f3207766f4afc65983fa759ffe},
  isbn = {978-1-59593-985-2},
  location = {Pittsburgh, PA, USA},
  pages = {157--166},
  publisher = {ACM},
  title = {Logsonomy - Social Information Retrieval with Logdata},
  url = {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},
  vgwort = {17},
  year = 2008
}

@inproceedings{hjss06bibsonomy,
  author = {Hotho, Andreas and J{\"a}schke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proc. of the ICCS 2006 Conceptual Structures Tool Interoperability
    Workshop},
  interhash = {5c3de78cf860c767ee40602b3a5382f3},
  intrahash = {4ca695ae4501bd56c03e435306d7c5a0},
  note = {(to appear)},
  title = {{BibSonomy}: A Social Bookmark and Publication Sharing System},
  year = 2006
}

@article{song2010limits,
  abstract = {A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
},
  author = {Song, Chaoming and Qu, Zehui and Blumm, Nicholas and Barabási, Albert-László},
  doi = {10.1126/science.1177170},
  eprint = {http://www.sciencemag.org/cgi/reprint/327/5968/1018.pdf},
  interhash = {f2611a08bf6db54f86e884c05f3cb5fb},
  intrahash = {a89330f8eb32ce62b5f5c9a2b4909f25},
  journal = {Science},
  number = 5968,
  pages = {1018--1021},
  title = {Limits of Predictability in Human Mobility},
  url = {http://www.barabasilab.com/pubs/CCNR-ALB_Publications/201002-19_Science-Predictability/201002-19_Science-Predictability.pdf},
  volume = 327,
  year = 2010
}

@inproceedings{breslin2009integrating,
  abstract = {Sensors have begun to infiltrate people's everyday lives. They can provide information about a car's condition, can enable smart buildings, and are being used in various mobile applications, to name a few. Generally, sensors provide information about various aspects of the real world. Online social networks, another emerging trend over the past six or seven years, can provide insights into the communication links and patterns between people. They have enabled novel developments in communications as well as transforming the Web from a technical infrastructure to a social platform, very much along the lines of the original Web as proposed by Tim Berners-Lee, which is now often referred to as the Social Web. In this position paper, we highlight some of the interesting research areas where sensors and social networks can fruitfully interface, from sensors providing contextual information in context-aware and personalized social applications, to using social networks as "storage infrastructures" for sensor information.},
  author = {Breslin, John G. and Decker, Stefan and Hauswirth, Manfred and Hynes, Gearoid and Phuoc, Danh Le and Passant, Alexandre and Polleres, Axel and Rabsch, Cornelius and Reynolds, Vinny},
  booktitle = {Proceedings on the W3C Workshop on the Future of Social Networking},
  interhash = {8a9846d06fcb3d48e5f081801a957565},
  intrahash = {e5286c49f4a49bb8752d473f126824dd},
  title = {Integrating Social Networks and Sensor Networks},
  url = {http://www.w3.org/2008/09/msnws/papers/sensors.html},
  year = 2009
}

@inproceedings{krause2008comparison,
  abstract = {Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s
data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. 

In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings.

Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems
to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space
retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.},
  address = {Heidelberg},
  author = {Krause, Beate and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008},
  editor = {Macdonald, Craig and Ounis, Iadh and Plachouras, Vassilis and Ruthven, Ian and White, Ryen W.},
  interhash = {37598733b747093d97a0840a11beebf5},
  intrahash = {86dbae2e531c7f68592f5780b46b6a70},
  pages = {101-113},
  publisher = {Springer},
  series = {LNAI},
  title = {A Comparison of Social Bookmarking  with Traditional Search},
  volume = 4956,
  year = 2008
}

@inproceedings{hotho2006bibsonomy,
  abstract = {Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. The reason for their immediate success is the
fact that no specific skills are needed for participating. In this
paper we specify a formal model for folksonomies and briefly describe 
our own system BibSonomy, which allows for sharing both bookmarks
and publication references in a kind of personal library.},
  address = {Aalborg},
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures},
  editor = {de Moor, Aldo and Polovina, Simon and Delugach, Harry},
  interhash = {d28c9f535d0f24eadb9d342168836199},
  intrahash = {5854a71547051543dd3d3d5e2e2f2b67},
  isbn = {87-7307-769-0},
  pages = {87-102},
  publisher = {Aalborg Universitetsforlag},
  title = {{BibSonomy}: A Social Bookmark and Publication Sharing System},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006bibsonomy.pdf},
  year = 2006
}

@proceedings{alani2007workshop,
  address = {Banff, Canada},
  editor = {Alani, Harith and Noy, Natasha and Stumme, Gerd and Mika, Peter and Sure, York and Vrandecic, Denny},
  interhash = {377c136010d79673ae5aaf7cf0cd84cb},
  intrahash = {aac9110f5a4e2e00028f0466555ce8d4},
  title = {Workshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007) at WWW 2007},
  url = {http://www2007.org/workshop-W7.php},
  year = 2007
}