@techreport{heymann2006collaborative,
  abstract = {Collaborative tagging systems---systems where many casual users annotate objects with free-form strings (tags) of their choosing---have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems.},
  author = {Heymann, Paul and Garcia-Molina, Hector},
  file = {heymann2006collaborative.pdf:heymann2006collaborative.pdf:PDF},
  groups = {public},
  institution = {Computer Science Department, Standford University},
  interhash = {d77846b40aadb0e25233cabf905bb93e},
  intrahash = {a6010ad0fef7cb1442298402ebb979b6},
  lastdatemodified = {2007-04-27},
  lastname = {Heymann},
  month = {April},
  own = {own},
  pdf = {heyman06-collaborative.pdf},
  read = {notread},
  timestamp = {2007-05-25 16:05:53},
  title = {Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems},
  url = {dbpubs.stanford.edu:8090/pub/2006-10},
  username = {dbenz},
  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
}

@article{limpens2008bridging,
  abstract = {Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations : tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This article compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web.},
  author = {Limpens, Freddy and Gandon, Fabien and Buffa, Michel},
  doi = {10.1109/ASEW.2008.4686305},
  file = {limpens2008bridging.pdf:limpens2008bridging.pdf:PDF},
  groups = {public},
  interhash = {cb1d534be80d664a50df66e8977b774e},
  intrahash = {9372f9c2db8b9f4cf05b3db84e6589ac},
  journal = {Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on},
  journalpub = {1},
  month = {Sept.},
  pages = {13-18},
  timestamp = {2009-07-24 14:21:18},
  title = {Bridging ontologies and folksonomies to leverage knowledge sharing on the social Web: A brief survey},
  username = {dbenz},
  year = 2008
}

@article{lux2008from,
  abstract = {Is Web 2.0 just hype or just a buzzword, which might disappear in the near future One way to find answers to these questions is to investigate the actual benefit of the Web 2.0 for real use cases. Within this contribution we study a very special aspect of the Web 2.0 the folksonomy and its use within self-directed learning. Guided by conceptual principles of emergent computing we point out methods, which might be able to let semantics emerge from folksonomies and discuss the effect of the results in self-directed learning.},
  author = {Lux, Mathias and Dösinger, Gisela},
  doi = {10.1504/IJKL.2007.016709},
  groups = {public},
  interhash = {5dde7a91231320f96c0c4b3e7ba9a503},
  intrahash = {dd5cdcc6449d97622033bbebcd4d1874},
  journal = {International Journal of Knowledge and Learning},
  journalpub = {1},
  month = jan,
  number = {4-5},
  pages = {515--528},
  timestamp = {2010-08-11 07:26:38},
  title = {From folksonomies to ontologies: employing wisdom of the crowds to serve learning purposes},
  url = {http://www.ingentaconnect.com/content/ind/ijkl/2008/00000003/F0020004/art00009},
  username = {dbenz},
  volume = 3,
  year = 2008
}

@inproceedings{marlow2006position,
  abstract = {In recent years, tagging systems have become increasingly popular. These systems enable users to add keywords (i.e., �tags�) to Internet resources (e.g., web pages, images, videos) without relying on a controlled vocabulary. Tagging systems have the potential to improve search, spam detection, reputation systems, and personal organization while introducing new modalities of social communication and opportunities for data mining. This potential is largely due to the social structure that underlies many of the current systems. Despite the rapid expansion of applications that support tagging of resources, tagging systems are still not well studied or understood. In this paper, we provide a short description of the academic related work to date. We offer a model of tagging systems, specifically in the context of web-based systems, to help us illustrate the possible benefits of these tools. Since many such systems already exist, we provide a taxonomy of tagging systems to help inform their analysis and design, and thus enable researchers to frame and compare evidence for the sustainability of such systems. We also provide a simple taxonomy of incentives and contribution models to inform potential evaluative frameworks. While this work does not present comprehensive empirical results, we present a preliminary study of the photosharing and tagging system Flickr to demonstrate our model and explore some of the issues in one sample system. This analysis helps us outline and motivate possible future directions of research in tagging systems.},
  address = {Edinburgh, Scotland},
  author = {Marlow, Cameron and Naaman, Mor and Boyd, Danah and Davis, Marc},
  booktitle = {Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006},
  file = {marlow2006position.pdf:marlow2006position.pdf:PDF},
  groups = {public},
  interhash = {7446351e0d902ee4f36fb750f82c50a5},
  intrahash = {d9f433de0945351fa2157c1424d9fe67},
  lastdatemodified = {2006-07-17},
  lastname = {Marlow},
  month = May,
  own = {own},
  pdf = {marlow06-tagging.pdf},
  read = {readnext},
  timestamp = {2007-09-11 13:31:31},
  title = {{Position Paper, Tagging, Taxonomy, Flickr, Article, ToRead}},
  url = {http://.rawsugar.com/www2006/cfp.html},
  username = {dbenz},
  year = 2006
}

@article{meo2009exploitation,
  abstract = {In this paper we present a new approach to supporting users to annotate and browse resources referred by a folksonomy. Our approach is characterized by the following novelties: (i) it proposes a probabilistic technique to quickly and accurately determine the similarity and the generalization degrees of two tags; (ii) it proposes two hierarchical structures and two related algorithms to arrange groups of semantically related tags in a hierarchy; this allows users to visualize tags of their interests according to desired semantic granularities and, then, helps them to find those tags best expressing their information needs. In this paper we first illustrate the technical characteristics of our approach; then we describe various experiments allowing its performance to be tested; finally, we compare it with other related approaches already proposed in the literature.},
  address = {Oxford, UK, UK},
  author = {Meo, Pasquale De and Quattrone, Giovanni and Ursino, Domenico},
  doi = {http://dx.doi.org/10.1016/j.is.2009.02.004},
  file = {meo2009exploitation.pdf:meo2009exploitation.pdf:PDF},
  groups = {public},
  interhash = {106972d128b1ec0f9d66e2edf1590d0d},
  intrahash = {014f9b4d75c01fa83bfa5eb703eea2d4},
  issn = {0306-4379},
  journal = {Inf. Syst.},
  journalpub = {1},
  number = 6,
  pages = {511--535},
  publisher = {Elsevier Science Ltd.},
  timestamp = {2009-12-17 14:17:03},
  title = {Exploitation of semantic relationships and hierarchical data structures to support a user in his annotation and browsing activities in folksonomies},
  url = {http://portal.acm.org/citation.cfm?id=1542755},
  username = {dbenz},
  volume = 34,
  year = 2009
}

@inproceedings{mori2006extracting,
  abstract = {Social networks have recently garnered considerable interest. With the intention of utilizing social networks for the Semantic Web, several studies have examined automatic extraction of social networks. However, most methods have addressed extraction of the strength of relations. Our goal is extracting the underlying relations between entities that are embedded in social networks. To this end, we propose a method that automatically extracts labels that describe relations among entities. Fundamentally, the method clusters similar entity pairs according to their collective contexts in Web documents. The descriptive labels for relations are obtained from results of clustering. The proposed method is entirely unsupervised and is easily incorporated into existing social network extraction methods. Our method also contributes to ontology population by elucidating relations between instances in social networks. Our experiments conducted on entities in political social networks achieved clustering with high precision and recall. We extracted appropriate relation labels to represent the entities.},
  author = {Mori, Junichiro and Tsujishita, Takumi and Matsuo, Yutaka and Ishizuka, Mitsuru},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {International Semantic Web Conference},
  crossref = {DBLP:conf/semweb/2006},
  ee = {http://dx.doi.org/10.1007/11926078_35},
  file = {mori2006extracting.pdf:mori2006extracting.pdf:PDF},
  groups = {public},
  interhash = {457973d894180bd95e99bb6f7bb5cbc5},
  intrahash = {f1a145a60c3e4d39e91b39a7c1178110},
  pages = {487-500},
  timestamp = {2009-06-01 15:32:20},
  title = {Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts},
  username = {dbenz},
  year = 2006
}

@inproceedings{tang2009towards,
  abstract = {A folksonomy refers to a collection of user-defined tags with which users describe contents published on the Web. With the flourish of Web 2.0, folksonomies have become an important mean to develop the Semantic Web. Because tags in folksonomies are authored freely, there is a need to understand the structure and semantics of these tags in various applications. In this paper, we propose a learning approach to create an ontology that captures the hierarchical semantic structure of folksonomies. Our experimental results on two different genres of real world data sets show that our method can effectively learn the ontology structure from the folksonomies.},
  address = {San Francisco, CA, USA},
  author = {Tang, Jie and fung Leung, Ho and Luo, Qiong and Chen, Dewei and Gong, Jibin},
  booktitle = {IJCAI'09: Proceedings of the 21st international jont conference on Artifical intelligence},
  file = {tang2009towards.pdf:tang2009towards.pdf:PDF},
  groups = {public},
  interhash = {17f95a6ba585888cf45443926d8b7e98},
  intrahash = {7b335f08a288a79eb70eff89f1ec7630},
  location = {Pasadena, California, USA},
  pages = {2089--2094},
  publisher = {Morgan Kaufmann Publishers Inc.},
  timestamp = {2009-12-23 21:30:44},
  title = {Towards ontology learning from folksonomies},
  url = {http://ijcai.org/papers09/Papers/IJCAI09-344.pdf},
  username = {dbenz},
  year = 2009
}

@inproceedings{schmitz2006mining,
  abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.},
  address = {Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and J�schke, Robert and Stumme, Gerd},
  booktitle = {Data Science and Classification. Proceedings of the 10th IFCS Conf.},
  editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and �iberna, A.},
  file = {schmitz2006mining.pdf:schmitz2006mining.pdf:PDF},
  interhash = {9f407e0b779aba5b3afca7fb906f579b},
  intrahash = {91a9a847b72a77e8f7d7db4de52716e5},
  lastdatemodified = {2006-12-07},
  lastname = {Schmitz},
  month = {July},
  own = {notown},
  pages = {261--270},
  pdf = {schmitz06-mining.pdf},
  publisher = {Springer},
  read = {notread},
  series = {Studies in Classification, Data Analysis, and Knowledge Organization},
  title = {Mining Association Rules in Folksonomies},
  year = 2006
}

@inproceedings{veres2006concept,
  abstract = {The recent popularity of social software in the wake of the much hyped "Web2.0" has resulted in a flurry of activity around folksonomies, the emergent systems of classification that result from making public the individual users’ personal classifications in the form of simple free form "tags". Several approaches have emerged in the analysis of these folksonomies including mathematical approaches for clustering and identifying affinities, social theories about cultural factors in tagging, and cognitive theories about their mental underpinnings. In this paper we argue that the most useful analysis is in terms of mental phenomena since naive classification is essentially a cognitive task. We then describe a method for extracting structural properties of free form user tags, based on the linguistic properties of the tags. This reveals some deep insights in the conceptual modeling behavior of naive users. Finally we explore the usefulness of the latent structural properties of free form "tag clouds" for interoperability between folksonomies from different services.},
  author = {Veres, C.},
  booktitle = {Conceptual Modeling - ER 2006},
  file = {veres2006concept.pdf:veres2006concept.pdf:PDF},
  groups = {public},
  interhash = {ce1a0dcac78702811f22fe3dc41bc46e},
  intrahash = {13540d1afb327c09e9c894a011b6450a},
  lastdatemodified = {2007-01-08},
  lastname = {Veres},
  own = {notown},
  pages = {325--338},
  pdf = {veres06-concept.pdf},
  read = {notread},
  timestamp = {2009-09-02 13:26:48},
  title = {Concept Modeling by the Masses: Folksonomy Structure and Interoperability},
  url = {http://dx.doi.org/10.1007/11901181_25},
  username = {dbenz},
  year = 2006
}

@inproceedings{wagner2010wisdom,
  abstract = {Although one might argue that little wisdom can be conveyed in messages of 140 characters or less, this paper sets out to explore whether the aggregation of messages in social awareness streams, such as Twitter, conveys meaningful information about a given domain. As a research community, we know little about the structural and semantic properties of such streams, and how they can be analyzed, characterized and used. This paper introduces a network-theoretic model of social awareness stream, a so-called \tweetonomy", together with a set of stream-based measures that allow researchers to systematically define and compare different stream aggregations. We apply the model and measures to a dataset acquired from Twitter to study emerging semantics in selected streams. The network-theoretic model and the corresponding measures introduced in this paper are relevant for researchers interested in information retrieval and ontology learning from social awareness streams. Our empirical findings demonstrate that different social awareness stream aggregations exhibit interesting differences, making them amenable for different applications.},
  author = {Wagner, C. and Strohmaier, M.},
  booktitle = {Proc. of the Semantic Search 2010 Workshop (SemSearch2010)},
  file = {wagner2010wisdom.pdf:wagner2010wisdom.pdf:PDF},
  groups = {public},
  interhash = {02c222a4f9abd5964ea61af034769af4},
  intrahash = {2f96232a648d4fd1617c389d899f3d2b},
  location = {Raleigh, NC, USA},
  month = {april},
  timestamp = {2010-04-19 08:03:47},
  title = {The Wisdom in Tweetonomies: Acquiring Latent Conceptual Structures from Social Awareness Streams},
  url = {http://mstrohm.wordpress.com/2010/04/17/on-taxonomies-folksonomies-and-tweetonomies/},
  username = {dbenz},
  year = 2010
}

@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{zhou2008unsupervised,
  abstract = {This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotationservices have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largelylowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might becomea key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations,for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervisedmodel to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.usas example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We furtherapply our model on another data set from Flickr to testify our model’s applicability on different environments. The experimentalresults demonstrate our model’s efficiency.},
  author = {Zhou, Mianwei and Bao, Shenghua and Wu, Xian and Yu, Yong},
  file = {zhou2008unsupervised.pdf:zhou2008unsupervised.pdf:PDF},
  interhash = {e8397fd51d43531b91e81776c879f487},
  intrahash = {ee6da1cc1300cf4fb68fc58d5e2bb819},
  journal = {The Semantic Web},
  pages = {680--693},
  title = {An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations},
  url = {http://dx.doi.org/10.1007/978-3-540-76298-0_49},
  year = 2008
}

@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
}

@article{golder2006usage,
  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 dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.},
  author = {Golder, Scott A. and Huberman, Bernardo A.},
  doi = {10.1177/0165551506062337},
  eprint = {http://jis.sagepub.com/cgi/reprint/32/2/198.pdf},
  file = {golder2006usage.pdf:golder2006usage.pdf:PDF},
  interhash = {df675e16fcba9cd0f6afc5c9f2a8a723},
  intrahash = {f67d3599f5282425b8e0e5b383d436a0},
  journal = {Journal of Information Science},
  number = 2,
  pages = {198--208},
  title = {Usage patterns of collaborative tagging systems},
  url = {http://jis.sagepub.com/cgi/content/abstract/32/2/198},
  volume = 32,
  year = 2006
}

@inproceedings{hotho2006information,
  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. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies,called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to findcommunities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.},
  address = {Heidelberg},
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {The Semantic Web: Research and Applications},
  editor = {Sure, York and Domingue, John},
  file = {hotho2006information.pdf:hotho2006information.pdf:PDF},
  groups = {public},
  interhash = {10ec64d80b0ac085328a953bb494fb89},
  intrahash = {3c301945817681d637ee43901c016939},
  month = {June},
  pages = {411-426},
  pdf = {hotho2006information.pdf},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  timestamp = {2010-11-11 15:34:51},
  title = {Information Retrieval in Folksonomies: Search and Ranking},
  username = {dbenz},
  volume = 4011,
  year = 2006
}

@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
}