@inproceedings{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},
  groups = {public},
  interhash = {e8397fd51d43531b91e81776c879f487},
  intrahash = {ee6da1cc1300cf4fb68fc58d5e2bb819},
  journal = {The Semantic Web},
  pages = {680--693},
  timestamp = {2009-09-24 23:27:32},
  title = {An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations},
  url = {http://dx.doi.org/10.1007/978-3-540-76298-0_49},
  username = {dbenz},
  year = 2008
}

@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
}

@article{eda2009effectiveness,
  abstract = {In this paper, we evaluate the effectiveness of a semantic smoothing technique to organize folksonomy tags. Folksonomy tags have no explicit relations and vary because they form uncontrolled vocabulary. We discriminates so-called subjective tags like “cool�? and “fun�? from folksonomy tags without any extra knowledge other than folksonomy triples and use the level of tag generalization to form the objective tags into a hierarchy.We verify that entropy of folksonomy tags is an effective measure for discriminating subjective folksonomy tags. Our hierarchical tag allocation method guarantees the number of children nodes and increases the number of available paths to a target node compared to an existing tree allocation method for folksonomy tags.},
  author = {Eda, Takeharu and Yoshikawa, Masatoshi and Uchiyama, Toshio and Uchiyama, Tadasu},
  ee = {http://dx.doi.org/10.1007/s11280-009-0069-1},
  file = {eda2009effectiveness.pdf:eda2009effectiveness.pdf:PDF},
  groups = {public},
  interhash = {a560796c977bc7582017f662bf88c16d},
  intrahash = {ec3c256e7d1f24cd9d407d3ce7e41d96},
  journal = {World Wide Web},
  journalpub = {1},
  number = 4,
  pages = {421-440},
  timestamp = {2010-08-15 15:00:40},
  title = {The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags.},
  url = {http://dblp.uni-trier.de/db/journals/www/www12.html#EdaYUU09},
  username = {dbenz},
  volume = 12,
  year = 2009
}

@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
}

@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{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
}