@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 Jäschke, 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},
  groups = {public},
  interhash = {9f407e0b779aba5b3afca7fb906f579b},
  intrahash = {ed504c16bc4eb561a9446bd98b10dca1},
  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},
  timestamp = {2007-09-11 13:31:35},
  title = {Mining Association Rules in Folksonomies},
  username = {dbenz},
  year = 2006
}

@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{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{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
}