@inproceedings{bullinaria2008semantic,
  author = {Bullinaria, J.A.},
  file = {bullinaria2008semantic.pdf:bullinaria2008semantic.pdf:PDF},
  groups = {public},
  interhash = {cdb7b1ff0e89f61f84e2c15a0e46c221},
  intrahash = {efae206c0f89363a3273a8d57c87eff5},
  journal = {ESSLLI Workshop on Distributional Lexical Semantics},
  timestamp = {2011-01-28 09:53:43},
  title = {Semantic Categorization Using Simple Word Co-occurrence statistics},
  username = {dbenz},
  year = 2008
}

@inproceedings{christiaens2006metadata,
  abstract = {In this paper we give a brief overview of different metadata mechanisms (like ontologies and folksonomies) and how they relate to each other. We identify major strengths and weaknesses of these mechanisms. We claim that these mechanisms can be classified from restricted (e.g., ontology) to free (e.g., free text tagging). In our view, these mechanisms should not be used in isolation, but rather as complementary solutions, in a continuous process wherein the strong points of one increase the semantic depth of the other. We give an overview of early active research already going on in this direction and propose that methodologies to support this process be developed. We demonstrate a possible approach, in which we mix tagging, taxonomy and ontology.},
  author = {Christiaens, Stijn},
  booktitle = {Lecture Notes in Computer Science: On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops},
  file = {christiaens2006metadata.pdf:christiaens2006metadata.pdf:PDF},
  groups = {public},
  interhash = {f733d993459329ed1ef9f26d303ba0d9},
  intrahash = {efc1396e845f3db1688dc8ef154d9520},
  lastdatemodified = {2007-01-04},
  lastname = {Christiaens},
  own = {notown},
  pdf = {christiaens06-metadata.pdf},
  publisher = {Springer},
  read = {notread},
  timestamp = {2007-09-11 13:31:23},
  title = {Metadata Mechanisms: From Ontology to Folksonomy ... and Back},
  url = {http://www.springerlink.com/content/m370107220473394},
  username = {dbenz},
  workshoppub = {1},
  year = 2006
}

@misc{asur2010predicting,
  abstract = {In recent years, social media has become ubiquitous and important for socialnetworking and content sharing. And yet, the content that is generated fromthese websites remains largely untapped. In this paper, we demonstrate howsocial media content can be used to predict real-world outcomes. In particular,we use the chatter from Twitter.com to forecast box-office revenues for movies.We show that a simple model built from the rate at which tweets are createdabout particular topics can outperform market-based predictors. We furtherdemonstrate how sentiments extracted from Twitter can be further utilized toimprove the forecasting power of social media.},
  author = {Asur, Sitaram and Huberman, Bernardo A.},
  file = {asur2010predicting.pdf:asur2010predicting.pdf:PDF},
  groups = {public},
  interhash = {538607d6d5da7946a0c5a2114a7c44f5},
  intrahash = {9c23c0465529a60d9540ee29e74856f1},
  note = {cite arxiv:1003.5699},
  timestamp = {2010-11-09 10:12:57},
  title = {Predicting the Future with Social Media},
  url = {http://arxiv.org/abs/1003.5699},
  username = {dbenz},
  year = 2010
}

@inproceedings{heymann2010tagging,
  abstract = {A fundamental premise of tagging systems is that regular users can organize large collections for browsing and other tasks using uncontrolled vocabularies. Until now, that premise has remained relatively unexamined. Using library data, we test the tagging approach to organizing a collection. We find that tagging systems have three major large scale organizational features: consistency, quality, and completeness. In addition to testing these features, we present results suggesting that users produce tags similar to the topics designed by experts, that paid tagging can effectively supplement tags in a tagging system, and that information integration may be possible across tagging systems.},
  author = {Heymann, Paul and Paepcke, Andreas and Garcia-Molina, Hector},
  booktitle = {WSDM},
  crossref = {conf/wsdm/2010},
  date = {2010-02-18},
  editor = {Davison, Brian D. and Suel, Torsten and Craswell, Nick and Liu, Bing},
  ee = {http://doi.acm.org/10.1145/1718487.1718495},
  file = {:heyman2010tagging.pdf:PDF},
  groups = {public},
  interhash = {d4f72ed57e6b99dbe32e18e218d81ef5},
  intrahash = {12579231cd5449f9a40cba9924975f09},
  isbn = {978-1-60558-889-6},
  pages = {51-60},
  publisher = {ACM},
  timestamp = {2010-04-08 07:27:02},
  title = {Tagging human knowledge.},
  url = {http://dblp.uni-trier.de/db/conf/wsdm/wsdm2010.html#HeymannPG10},
  username = {dbenz},
  year = 2010
}

@article{cattuto2006semiotic,
  abstract = {Abstract A distributed classification paradigm known as collaborative tagging has been successfully deployed in large-scale web applications designed to manage and share diverse online resources. Users of these applications organize resources by associating with them freely chosen text labels, or tags. Here we regard tags as basic dynamical entities and study the semiotic dynamics underlying collaborative tagging. We collect data from a popular system and focus on tags associated with a given resource. We find that the frequencies of tags obey to a generalized Zipf�s law and show that a Yule�Simon process with memory can be used to explain the observed frequency distributions in terms of a simple model of user behavior},
  author = {Cattuto, Ciro},
  file = {cattuto2006semiotic.pdf:cattuto2006semiotic.pdf:PDF},
  groups = {public},
  interhash = {6651fe8b8916e8407f738325c092b860},
  intrahash = {df2a1161a75e3f328f82c204f942bb8a},
  journal = {The European Physical Journal C - Particles and Fields},
  journalpub = {1},
  lastdatemodified = {2006-09-25},
  lastname = {Cattuto},
  longnotes = {doi:10.1140/epjcd/s2006-03-004-4},
  month = {August},
  own = {notown},
  pages = {33--37},
  pdf = {cattuto2006-semiotic.pdf},
  read = {notread},
  timestamp = {2007-09-11 13:31:22},
  title = {Semiotic dynamics in online social communities},
  url = {http://www.springerlink.com/content/t964j63030507341},
  username = {dbenz},
  volume = 46,
  year = 2006
}

@article{cattuto2007semiotic,
  abstract = {Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing vast amounts of information. Here we collect data from a popular system and investigate the statistical properties of tag co-occurrence. We introduce a stochastic model of user behavior embodying two main aspects of collaborative tagging: (i) a frequency-bias mechanism related to the idea that users are exposed to each other's tagging activity; (ii) a notion of memory - or aging of resources - in the form of a heavy-tailed access to the past state of the system. Remarkably, our simple modeling is able to account quantitatively for the observed experimental features, with a surprisingly high accuracy. This points in the direction of a universal behavior of users, who - despite the complexity of their own cognitive processes and the uncoordinated and selfish nature of their tagging activity - appear to follow simple activity patterns.},
  author = {Cattuto, Ciro and Loreto, Vittorio and Pietronero, Luciano},
  file = {cattuto2007semiotic.pdf:cattuto2007semiotic.pdf:PDF},
  groups = {public},
  interhash = {189402152f540f931a0eea5b8538411f},
  intrahash = {95a8e6a348e0acde9ce781004c45b94e},
  journal = {Proceedings of the National Academy of Sciences United States of America},
  journalpub = {1},
  lastdatemodified = {2007-05-14},
  lastname = {Cattuto},
  own = {notown},
  pages = 1461,
  pdf = {cattuto06-collaborative.pdf},
  read = {notread},
  timestamp = {2007-09-11 13:31:22},
  title = {Semiotic Dynamics and Collaborative Tagging},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0605015},
  username = {dbenz},
  volume = 104,
  year = 2007
}

@inproceedings{haase2005usagedriven,
  abstract = {Large information repositories as digital libraries, online shops, etc. rely on a taxonomy of the objects under consideration to structure the vast contents and facilitate browsing and searching (e.g., ACM topic classification for computer science literature, Amazon product taxonomy, etc.). As in heterogeneous communities users typically will use different parts of such an ontology with varying intensity, customization and personalization of the ontologies is desirable. In this paper we adapt a collaborative filtering recommender system to assist users in the management and evolution of their personal ontology by providing detailed suggestions of ontology changes. Such a system has been implemented in the context of Bibster, a peer-to-peer based personal bibliography management tool. Finally, we report on an in-situ experiment with the Bibster community that shows the performance improvements over non-personalized recommendations.},
  address = {Las Vegas, Nevada USA},
  author = {Haase, Peter and Hotho, Andreas and Schmidt-Thieme, Lars and Sure, York},
  booktitle = {Proceedings of the 3rd International Conference on Universal A ccess in Human-Computer Interaction (UAHCI)},
  file = {haase2005usagedriven.pdf:haase2005usagedriven.pdf:PDF},
  groups = {public},
  interhash = {e5681c379cdfe126e44d034dac3fddad},
  intrahash = {05b1daa45aedb5c1f63683f961f17a9e},
  lastdatemodified = {2006-07-06},
  lastname = {Haase},
  month = {22-27 July},
  own = {notown},
  pdf = {Haase05.pdf},
  read = {notread},
  timestamp = {2007-05-25 16:05:53},
  title = {Usage-driven Evolution of Personal Ontologies},
  username = {dbenz},
  year = 2005
}

@inproceedings{jung2004collaborative,
  abstract = {This paper proposes the collaborative web browsing system sharing knowledge with other users. We have specifically focused on user interests extracted from bookmarks. A simple URL based-bookmark is provided with structural information by the conceptualization of the ontology. Furthermore, ontology learning based on a hierarchical clustering method can be applied to handle dynamic changes in bookmarks. As a result of our experiments, with respect to recall, about 53.1% of the total time was saved during collaborative browsing for seeking the equivalent set of information, as compared with single web browsing.},
  author = {Jung, Jason J. and Yu, Young-Hoon and Jo, GeunSik},
  booktitle = {International Conference on Computational Science},
  file = {jung2004collaborative.pdf:jung2004collaborative.pdf:PDF},
  groups = {public},
  interhash = {e1b4ebe8a3ae831c9372c7ca4f042256},
  intrahash = {ff3838b29b4d15654173fc724dcd383a},
  lastdatemodified = {2005-08-06},
  lastname = {Jung},
  note = {bibsource: DBLP, http://dblp.uni-trier.de},
  own = {own},
  pages = {513-520},
  pdf = {jung04.pdf},
  read = {read},
  timestamp = {2007-05-25 16:05:53},
  title = {Collaborative Web Browsing Based on Ontology Learning from Bookmarks.},
  url = {springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3038{\&}spage=513},
  username = {dbenz},
  year = 2004
}

@misc{leydesdorff2010semantic,
  abstract = { Meaning can be generated when information is related at a systemic level.Such a system can be an observer, but also a discourse, for example,operationalized as a set of documents. The measurement of semantics assimilarity in patterns (correlations) and latent variables (factor analysis)has been enhanced by computer techniques and the use of statistics; forexample, in "Latent Semantic Analysis". This communication provides anintroduction, an example, pointers to relevant software, and summarizes thechoices that can be made by the analyst. Visualization ("semantic mapping") isthus made more accessible.},
  author = {Leydesdorff, Loet and Welbers, Kasper},
  file = {leydesdorff2010semantic.pdf:leydesdorff2010semantic.pdf:PDF},
  groups = {public},
  interhash = {ae82f513935ffa158395683303d52517},
  intrahash = {cee25eef0438b6ac2998041dbd93016e},
  note = {cite arxiv:1011.5209},
  timestamp = {2010-11-24 10:21:03},
  title = {The semantic mapping of words and co-words in contexts},
  url = {http://arxiv.org/abs/1011.5209},
  username = {dbenz},
  year = 2010
}

@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
}

@inproceedings{ramage2009clustering,
  abstract = {Automatically clustering web pages into semantic groups promises improved search and browsing on the web. In this paper, we demonstrate how user-generated tags from largescale social bookmarking websites such as del.icio.us can be used as a complementary data source to page text and anchor text for improving automatic clustering of web pages. This paper explores the use of tags in 1) K-means clustering in an extended vector space model that includes tags as well as page text and 2) a novel generative clustering algorithm based on latent Dirichlet allocation that jointly models text and tags. We evaluate the models by comparing their output to an established web directory. We find that the naive inclusion of tagging data improves cluster quality versus page text alone, but a more principled inclusion can substantially improve the quality of all models with a statistically significant absolute F-score increase of 4%. The generative model outperforms K-means with another 8% F-score increase.},
  address = {New York, NY, USA},
  author = {Ramage, Daniel and Heymann, Paul and Manning, Christopher D. and Garcia-Molina, Hector},
  booktitle = {WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining},
  doi = {http://doi.acm.org/10.1145/1498759.1498809},
  file = {ramage2009clustering.pdf:ramage2009clustering.pdf:PDF},
  groups = {public},
  interhash = {5595f06f88310ed67fd6fe23f813c69b},
  intrahash = {75c4bad29d7eb4b34f68da27f0353516},
  isbn = {978-1-60558-390-7},
  location = {Barcelona, Spain},
  pages = {54--63},
  publisher = {ACM},
  timestamp = {2009-04-24 10:19:45},
  title = {Clustering the tagged web},
  url = {http://portal.acm.org/citation.cfm?id=1498809},
  username = {dbenz},
  year = 2009
}

@inproceedings{baezayates2007extracting,
  abstract = {In this paper we study a large query log of more than twenty million queries with the goal of extracting the semantic relations that are implicitly captured in the actions of users submitting queries and clicking answers. Previous query log analyses were mostly done with just the queries and not the actions that followed after them. We first propose a novel way to represent queries in a vector space based on a graph derived from the query-click bipartite graph. We then analyze the graph produced by our query log, showing that it is less sparse than previous results suggested, and that almost all the measures of these graphs follow power laws, shedding some light on the searching user behavior as well as on the distribution of topics that people want in the Web. The representation we introduce allows to infer interesting semantic relationships between queries. Second, we provide an experimental analysis on the quality of these relations, showing that most of them are relevant. Finally we sketch an application that detects multitopical URLs.},
  address = {New York, NY, USA},
  author = {Baeza-Yates, Ricardo and Tiberi, Alessandro},
  booktitle = {KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {http://doi.acm.org/10.1145/1281192.1281204},
  file = {baezayates2007extracting.pdf:baezayates2007extracting.pdf:PDF},
  groups = {public},
  interhash = {26ca034be705abaf072835784f53d877},
  intrahash = {6e45b65feffd1545c6dca62bf4b8f53d},
  isbn = {978-1-59593-609-7},
  location = {San Jose, California, USA},
  pages = {76--85},
  publisher = {ACM},
  timestamp = {2009-06-01 15:31:03},
  title = {Extracting semantic relations from query logs},
  url = {http://portal.acm.org/citation.cfm?id=1281192.1281204},
  username = {dbenz},
  year = 2007
}

@inproceedings{brank2005survey,
  abstract = {An ontology is an explicit formal conceptualization of some domain of interest. Ontologies are increasingly used in various fields such as knowledge management, information extraction, and the semantic web. Ontology evaluation is the problem of assessing a given ontology from the point of view of a particular criterion of application, typically in order to determine which of several ontologies would best suit a particular purpose. This paper presents a survey of the state of the art in ontology evaluation.},
  author = {Brank, Janez and Grobelnik, Marko and Mladeni{\'c}, Dunja},
  booktitle = {Proc. of 8th Int. multi-conf. Information Society},
  file = {brank2005survey.pdf:brank2005survey.pdf:PDF},
  groups = {public},
  interhash = {394d7ea166cc0745dc8682a65975648c},
  intrahash = {8c910a2d3f6708b23e03e06ff843c8a8},
  pages = {166--169},
  timestamp = {2010-02-23 12:55:04},
  title = {A Survey of Ontology Evaluation Techniques},
  username = {dbenz},
  year = 2005
}

@incollection{chung2006webbased,
  abstract = {Given that pairwise similarity computations are essential in ontology learning and data mining, we propose a similarity framework that is based on a conventional Web search engine. There are two main aspects that we can benefit from utilizing a Web search engine. First, we can obtain the freshest content for each term that represents the up-to-date knowledge on the term. This is particularly useful for dynamic ontology management in that ontologies must evolve with time as new concepts or terms appear. Second, in comparison with the approaches that use the certain amount of crawled Web documents as corpus, our method is less sensitive to the problem of data sparseness because we access as much content as possible using a search engine. At the core of our proposed methodology, we present two different measures for similarity computation, a mutual information based and a feature-based metric. Moreover, we show how the proposed metrics can be utilized for modifying existing ontologies. Finally, we compare the extracted similarity relations with semantic similarity using WordNet. Experimental results show that our method can extract topical relations between terms that are not present in conventional concept-based ontologies.},
  address = {Berlin / Heidelberg},
  affiliation = {Yahoo! Inc., 2821 Mission College Blvd, Santa Clara, CA 95054 USA USA},
  author = {Chung, Seokkyung and Jun, Jongeun and McLeod, Dennis},
  booktitle = {On the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE},
  doi = {10.1007/11914853_70},
  editor = {Meersman, Robert and Tari, Zahir},
  file = {chung2006webbased.pdf:chung2006webbased.pdf:PDF},
  groups = {public},
  interhash = {dc3c95f48bc109fce13b027443c8c96d},
  intrahash = {abc370e27adf82c389b3a96dbcffb866},
  pages = {1092-1109},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  timestamp = {2010-11-10 11:51:57},
  title = {A Web-Based Novel Term Similarity Framework for Ontology Learning},
  url = {http://dx.doi.org/10.1007/11914853_70},
  username = {dbenz},
  volume = 4275,
  workshoppub = {1},
  year = 2006
}

@incollection{garciasilva2008pattern,
  abstract = {With the goal of speeding up the ontology development process, ontology engineers are starting to reuse as much as possible available ontologies and non-ontological resources such as classification schemes, thesauri, lexicons and folksonomies, that already have some degree of consensus. The reuse of such non-ontological resources necessarily involves their re-engineering into ontologies. Non-ontological resources are highly heterogeneous in their data model and contents: they encode different types of knowledge, and they can be modeled and implemented in different ways. In this paper we present (1) a typology for non-ontological resources, (2) a pattern based approach for re-engineering non-ontological resources into ontologies, and (3) a use case of the proposed approach.},
  at = {2009-02-12 17:08:10},
  author = {Garc\'{i}a-Silva, Andr\'{e}s and G\'{o}mez-P\'{e}rez, Asunci\'{o}n and Su\'{a}rez-Figueroa, Mari and Villaz\'{o}n-Terrazas, Boris},
  doi = {http://dx.doi.org/10.1007/978-3-540-89704-0\_12},
  file = {garciasilva2008pattern.pdf:garciasilva2008pattern.pdf:PDF},
  groups = {public},
  interhash = {f09d71443dff615a314c435df89a3d39},
  intrahash = {98096132b1eb4b4b5b0de3cec6a22de5},
  journal = {The Semantic Web},
  misc_id = {4039576},
  pages = {167--181},
  priority = {2},
  timestamp = {2009-09-24 23:29:10},
  title = {A Pattern Based Approach for Re-engineering Non-Ontological Resources into Ontologies},
  url = {http://dx.doi.org/10.1007/978-3-540-89704-0\_12},
  username = {dbenz},
  year = 2008
}

@inproceedings{haase2005collaborative,
  abstract = {Large information repositories as digital libraries, online shops, etc. rely on a taxonomy of the objects under consideration to structure the vast contents and facilitate browsing and searching (e.g., ACM topic classification for computer science literature, Amazon product taxonomy, etc.). As in heterogenous communities users typically will use different parts of such an ontology with varying intensity, customization and personalization of the ontologies is desirable. Of particular interest for supporting users during the personalization are collaborative filtering systems which can produce personal recommendations by computing the similarity between own preferences and the one of other people. In this paper we adapt a collaborative filtering recommender system to assist users in the management and evolution of their personal ontology by providing detailed suggestions of ontology changes. Such a system has been implemented in the context of Bibster, a peer-to-peer based personal bibliography management tool. Finally, we report on an experiment with the Bibster community that shows the performance improvements over non-personalized recommendations.},
  author = {Haase, Peter and Hotho, Andreas and Schmidt-Thieme, Lars and Sure, York},
  booktitle = {ESWC},
  crossref = {conf/esws/2005},
  date = {2005-05-24},
  editor = {Gómez-Pérez, Asunción and Euzenat, Jérôme},
  ee = {http://dx.doi.org/10.1007/11431053_33},
  file = {haase2005collaborative.pdf:haase2005collaborative.pdf:PDF},
  groups = {public},
  interhash = {c9ba81293a1b27f1c9bdf38a3beec060},
  intrahash = {1a8829cde1cb26241a48901e28a953d2},
  isbn = {3-540-26124-9},
  pages = {486-499},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  timestamp = {2009-11-10 11:30:42},
  title = {Collaborative and Usage-Driven Evolution of Personal Ontologies.},
  url = {http://www.aifb.uni-karlsruhe.de/WBS/pha/publications/collaborative05eswc.pdf},
  username = {dbenz},
  volume = 3532,
  year = 2005
}

@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
}

@inproceedings{krause2008antisocial,
  abstract = {The annotation of web sites in social bookmarking systemshas become a popular way to manage and find informationon the web. The community structure of such systems attractsspammers: recent post pages, popular pages or specifictag pages can be manipulated easily. As a result, searchingor tracking recent posts does not deliver quality resultsannotated in the community, but rather unsolicited, oftencommercial, web sites. To retain the benefits of sharingone’s web content, spam-fighting mechanisms that can facethe flexible strategies of spammers need to be developed.},
  address = {New York, NY, USA},
  author = {Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd},
  booktitle = {AIRWeb '08: Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web},
  doi = {10.1145/1451983.1451998},
  file = {krause2008antisocial.pdf:krause2008antisocial.pdf:PDF},
  groups = {public},
  interhash = {a45d40ac7776551301ad9dde5b25357f},
  intrahash = {5b6b648fd25c15d594404ae26fcda6b4},
  isbn = {978-1-60558-159-0},
  location = {Beijing, China},
  month = apr,
  pages = {61--68},
  publisher = {ACM},
  timestamp = {2010-11-10 15:35:25},
  title = {The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems},
  url = {http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf},
  username = {dbenz},
  year = 2008
}

@article{siorpaes2008games,
  abstract = {Weaving the Semantic Web requires that humans contribute their labor and judgment for creating, extending, and updating formal knowledge structures. Hiding such tasks behind online multiplayer games presents the tasks as fun and intellectually challenging entertainment.},
  address = {Los Alamitos, CA, USA},
  author = {Siorpaes, Katharina and Hepp, Martin},
  doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2008.45},
  file = {siorpaes2008games.pdf:siorpaes2008games.pdf:PDF},
  groups = {public},
  interhash = {9852833e23b841db871ed6776f78922b},
  intrahash = {b5f12aecb395b0e5bf4b03b816a46c03},
  issn = {1541-1672},
  journal = {IEEE Intelligent Systems},
  journalpub = {1},
  number = 3,
  pages = {50-60},
  publisher = {IEEE Computer Society},
  timestamp = {2010-03-04 11:14:58},
  title = {Games with a Purpose for the Semantic Web},
  url = {http://www.computer.org/portal/web/csdl/doi/10.1109/MIS.2008.45},
  username = {dbenz},
  volume = 23,
  year = 2008
}