@inproceedings{aurnhammer2006augmenting,
  abstract = {We propose an approach that unifies browsing by tags and visual features for intuitive exploration of image databases. Incontrast to traditional image retrieval approaches, we utilise tags provided by users on collaborative tagging sites, complementedby simple image analysis and classification. This allows us to find new relations between data elements. We introduce theconcept of a navigation map, that describes links between users, tags, and data elements for the example of the collaborativetagging site Flickr. We show that introducing similarity search based on image features yields additional links on this map.These theoretical considerations are supported by examples provided by our system, using data and tags from real Flickr users.},
  author = {Aurnhammer, Melanie and Hanappe, Peter and Steels, Luc},
  file = {aurnhammer2006augmenting.pdf:aurnhammer2006augmenting.pdf:PDF},
  groups = {public},
  interhash = {a9d35e917da138f929b5d81f1dab4fd0},
  intrahash = {a286ce64106a503e135e7114365c77b2},
  journal = {The Semantic Web - ISWC 2006},
  pages = {58--71},
  timestamp = {2009-08-11 18:38:56},
  title = {Augmenting Navigation for Collaborative Tagging with Emergent Semantics},
  url = {http://dx.doi.org/10.1007/11926078_5},
  username = {dbenz},
  volume = 4273,
  year = 2006
}

@article{golder2006structurec,
  abstract = {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 dynamical 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 dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.},
  author = {Golder, Scott and Huberman, Bernardo A.},
  file = {golder2006structure.pdf:golder2006structure.pdf:PDF},
  groups = {public},
  interhash = {03565ad9c6fc315068e528a53ed158ae},
  intrahash = {f26e96f09d59ba7d33d5339fa5d4891b},
  journal = {Journal of Information Sciences},
  journalpub = {1},
  lastdatemodified = {2007-04-27},
  lastname = {Golder},
  month = {April},
  number = 2,
  own = {own},
  pages = {198--208},
  pdf = {golder06-structure.pdf},
  read = {readnext},
  timestamp = {2011-01-28 11:35:13},
  title = {The Structure of Collaborative Tagging Systems},
  url = {http://.hpl.hp.com/research/idl/papers/tags/index.html},
  username = {dbenz},
  volume = 32,
  year = 2006
}

@inproceedings{hotho2006das,
  abstract = {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.},
  address = {Baden-Baden},
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Social Software in der Wertschöpfung},
  file = {hotho2006das.pdf:hotho2006das.pdf:PDF},
  groups = {public},
  interhash = {1b39e4a77cac919f9030601711aad543},
  intrahash = {a333df6fdc7ff9322e3ce03988a7965e},
  pdf = {E:\home\help_of_all_helps.pdf},
  publisher = {Nomos},
  timestamp = {2009-09-29 12:35:44},
  title = {Das Entstehen von Semantik in BibSonomy},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006entstehen.pdf},
  username = {dbenz},
  year = 2006
}

@inproceedings{hotho2006emergent,
  abstract = {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.},
  address = {Bonn},
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Informatik 2006 -- Informatik für Menschen. Band 2},
  editor = {Hochberger, Christian and Liskowsky, Rüdiger},
  file = {hotho2006emergent.pdf:hotho2006emergent.pdf:PDF},
  groups = {public},
  interhash = {53e5677ab0bf1a8f5a635cc32c9082ba},
  intrahash = {05043cc20f1e0f5a612135c970e4f1ac},
  month = {October},
  note = {Proc. Workshop on Applications of Semantic Technologies, Informatik 2006},
  publisher = {Gesellschaft für Informatik},
  series = {Lecture Notes in Informatics},
  timestamp = {2009-09-14 18:13:04},
  title = {Emergent Semantics in BibSonomy},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006emergent.pdf},
  username = {dbenz},
  volume = {P-94},
  year = 2006
}

@techreport{kome2005hierarchical,
  abstract = {The growth in digital resource repositories flickr and del.icio.us, mirrors the growth of Folksonomies to support resource classification and access. Despite this phenomenon, little is known about the effectiveness of folksonomy for retrieval and organization. Little is also known about their structure and the types of semantic relationships among folksonomy terms. This study analyzes folksonomy metadata for hierarchal semantic relationships via a content analysis of approximately 2000 folksonomy tags in over 600 individual entries. The terms were classified into groups and analyzed for hierarchical relationships. The results indicate that hierarchical relationships are part of Folksonomies. The conclusion briefly explores the potential value of thesauri for Folksonomy development, and the value of Folksonomies to thesauri developers.},
  author = {Kome, Sam H.},
  file = {kome2005hierarchical.pdf:kome2005hierarchical.pdf:PDF},
  groups = {public},
  institution = {School of Information and Library Science},
  interhash = {15b120bb7d1576ef6fd2ef63668aed6a},
  intrahash = {e59c58d6b0f6b70dd8f8d1abf3a9f9fc},
  lastdatemodified = {2006-07-17},
  lastname = {Kome},
  month = {November},
  own = {own},
  pdf = {kome05-hierarchical.pdf},
  read = {readnext},
  timestamp = {2007-09-11 13:31:29},
  title = {Hierarchical Subject Relationships in Folksonomies},
  url = {hdl.handle.net/1901/238},
  username = {dbenz},
  year = 2005
}

@inproceedings{wu2006exploring,
  abstract = {In order to obtain a machine understandable semantics for web resources, research on the Semantic Web tries to an- notate web resources with concepts and relations from ex- plicitly de¯ned formal ontologies. This kind of formal an- notation is usually done manually or semi-automatically. In this paper, we explore a complement approach that focuses on the \social annotations of the web" which are annota- tions manually made by normal web users without a pre- de¯ned formal ontology. Compared to the formal annota- tions, although social annotations are coarse-grained, infor- mal and vague, they are also more accessible to more peo- ple and better re°ect the web resources' meaning from the users' point of views during their actual usage of the web re- sources. Using a social bookmark service as an example, we show how emergent semantics [2] can be statistically derived from the social annotations. Furthermore, we apply the de- rived emergent semantics to discover and search shared web bookmarks. The initial evaluation on our implementation shows that our method can e®ectively discover semantically related web bookmarks that current social bookmark service can not discover easily.},
  address = {New York, NY, USA},
  author = {Wu, Xian and Zhang, Lei and Yu, Yong},
  booktitle = {WWW '06: Proceedings of the 15th international conference on World Wide Web},
  file = {wu2006exploring.pdf:wu2006exploring.pdf:PDF},
  groups = {public},
  interhash = {478741551c92402f539a90a9caed61b6},
  intrahash = {2ff38a7f8e9e3941d0598877fe964eb5},
  lastdatemodified = {2007-01-04},
  lastname = {Wu},
  own = {notown},
  pages = {417--426},
  pdf = {wu06-exploring.pdf},
  publisher = {ACM Press},
  read = {notread},
  timestamp = {2007-09-11 13:31:41},
  title = {Exploring social annotations for the semantic web},
  url = {http://doi.acm.org/10.1145/1135777.1135839},
  username = {dbenz},
  year = 2006
}

@inproceedings{xu2006towards,
  abstract = {Content organization over the Internet went through several interesting phases of evolution: from structured directories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step towards the semantic web vision. Tagging allows ranking and data organization to directly utilize inputs from end users, enabling machine processing of Web content. Since tags are created by individual users in a free form, one important problem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set of general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popularity to ensure tag quality. We propose a collaborative tag suggestion algorithm using these criteria to spot high-quality tags. The proposed algorithm employs a goodness measure for tags derived from collective user authorities to combat spam. The goodness measure is iteratively adjusted by a reward-penalty algorithm, which also incorporates other sources of tags, e.g., content-based auto-generated tags. Our experiments based on My Web 2.0 show that the algorithm is effective.},
  address = {Edinburgh, Scotland},
  author = {Xu, Zhichen and Fu, Yun and Mao, Jianchang and Su, Difu},
  booktitle = {Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006},
  file = {xu2006towards.pdf:xu2006towards.pdf:PDF},
  groups = {public},
  interhash = {e18fd92b0ffa21b9f0cbb3a2fe15b873},
  intrahash = {49719d13c6da0c5f6917b97ef777184e},
  lastdatemodified = {2006-07-17},
  lastname = {Xu},
  month = May,
  own = {own},
  pdf = {xu06-towards.pdf},
  read = {readnext},
  timestamp = {2007-09-11 13:31:41},
  title = {Towards the Semantic Web: Collaborative Tag Suggestions},
  url = {http://.inf.unisi.ch/phd/mesnage/site/Readings/Readings.html},
  username = {dbenz},
  year = 2006
}

@article{cattuto2007network,
  abstract = {Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of these systems. We consider their underlying data structures - so-called folksonomies - as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them.Subsequently, we introduce a network of tag co-occurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.},
  address = {Amsterdam, The Netherlands},
  author = {Cattuto, Ciro and Schmitz, Christoph and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio and Hotho, Andreas and Grahl, Miranda and Stumme, Gerd},
  file = {cattuto2007network.pdf:cattuto2007network.pdf:PDF},
  groups = {public},
  interhash = {fc5f2df61d28bc99b7e15029da125588},
  intrahash = {1dfe8b2aa29adf4929cbb845950f78bc},
  issn = {0921-7126},
  journal = {AI Communications},
  journalpub = {1},
  month = dec,
  number = 4,
  pages = {245--262},
  publisher = {IOS Press},
  timestamp = {2010-11-10 15:35:25},
  title = {Network properties of folksonomies},
  url = {http://portal.acm.org/citation.cfm?id=1365538},
  username = {dbenz},
  volume = 20,
  year = 2007
}

@inproceedings{jaeschke2007analysis,
  abstract = {BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.},
  address = {Berlin, Heidelberg},
  author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007)},
  editor = {Priss, U. and Polovina, S. and Hill, R.},
  file = {jaeschke2007analysis.pdf:jaeschke2007analysis.pdf:PDF},
  groups = {public},
  interhash = {4352d1142afa561460511b22d4ce5103},
  intrahash = {0c2b212b9ea3d822bf4729fd5fe6b6e1},
  isbn = {3-540-73680-8},
  month = {July},
  pages = {283--295},
  publisher = {Springer-Verlag},
  series = {Lecture Notes in Artificial Intelligence},
  timestamp = {2010-11-10 15:35:25},
  title = {Analysis of the Publication Sharing Behaviour in {BibSonomy}},
  username = {dbenz},
  vgwort = {22},
  volume = 4604,
  year = 2007
}

@article{jaeschke2008discovering,
  abstract = {Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.},
  address = {New York},
  author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd},
  booktitle = {Semantic Web and Web 2.0},
  doi = {10.1016/j.websem.2007.11.004},
  editor = {Finin, T. and Mizoguchi, R. and Staab, S.},
  file = {jaeschke2008discovering.pdf:jaeschke2008discovering.pdf:PDF},
  groups = {public},
  interhash = {cfca594f9dbe30694bfbcdeb40dc4e88},
  intrahash = {18e8babe208fae2c0342438617b0ec31},
  issn = {1570-8268},
  journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
  journalpub = {1},
  month = feb,
  number = 1,
  pages = {38--53},
  publisher = {Elsevier},
  timestamp = {2010-11-10 15:35:25},
  title = {Discovering Shared Conceptualizations in Folksonomies},
  url = {http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b},
  username = {dbenz},
  vgwort = {59},
  volume = 6,
  year = 2008
}