@inproceedings{specia2007integrating,
  abstract = {While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified.},
  author = {Specia, Lucia and Motta, Enrico},
  file = {specia2007integrating.pdf:specia2007integrating.pdf:PDF},
  groups = {public},
  interhash = {b828fbd5c9ddc4f9551f973445ecb283},
  intrahash = {8800fc1a639aeb43fd55598d2410e2e1},
  pages = {624-639},
  publisher = {Springer Berlin / Heidelberg},
  series = {Lecture Notes in Computer Science},
  timestamp = {2007-09-29 15:16:09},
  title = {Integrating Folksonomies with the Semantic Web},
  username = {dbenz},
  volume = {4519/2007},
  year = 2007
}

@article{ryu2009toward,
  abstract = {This paper describes new thesaurus construction method in which class-based, small size thesauruses are constructed and merged as a whole based on domain classification system. This method has advantages in that 1) taxonomy construction complexity is reduced, 2) each class-based thesaurus can be reused in other domain thesaurus, and 3) term distribution per classes in target domain is easily identified. The method is composed of three steps: term extraction step, term classification step, and taxonomy construction step. All steps are balanced approaches of automatic processing and manual verification. We constructed Korean IT domain thesaurus based on proposed method. Because terms are extracted from Korean newspaper and patent corpus in IT domain, the thesaurus includes many Korean neologisms. The thesaurus consists of 81 upper level classes and over 1,000 IT terms.},
  author = {Ryu, P.M. and Kim, J.H. and Nam, Y. and Huang, J.X. and Shin, S. and Lee, S.M. and Choi, K.S.},
  file = {ryu2009toward.pdf:ryu2009toward.pdf:PDF},
  groups = {public},
  interhash = {33037e9884a62f1994c9d45eb68c27e7},
  intrahash = {bd4f375366e49a3eb31e60b268dca01c},
  journal = {Relation},
  journalpub = {1},
  number = {1.129},
  pages = 7396,
  publisher = {Citeseer},
  timestamp = {2010-11-09 12:05:09},
  title = {{Toward Domain Specific Thesaurus Construction: Divide-and-Conquer Method}},
  url = {http://scholar.google.de/scholar.bib?q=info:4K_xIsqmea0J:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=9},
  username = {dbenz},
  volume = 10,
  year = 2009
}

@article{gemmell2008personalizing,
  abstract = {The popularity of collaborative tagging, otherwise known as “folksonomies�?, emanate from the flexibility they afford usersin navigating large information spaces for resources, tags, or other users, unencumbered by a pre-defined navigational orconceptual hierarchy. Despite its advantages, social tagging also increases user overhead in search and navigation: usersare free to apply any tag they wish to a resource, often resulting in a large number of tags that are redundant, ambiguous,or idiosyncratic. Data mining techniques such as clustering provide a means to overcome this problem by learning aggregateuser models, and thus reducing noise. In this paper we propose a method to personalize search and navigation based on unsupervisedhierarchical agglomerative tag clustering. Given a user profile, represented as a vector of tags, the learned tag clustersprovide the nexus between the user and those resources that correspond more closely to the user’s intent. We validate thisassertion through extensive evaluation of the proposed algorithm using data from a real collaborative tagging Web site.},
  author = {Gemmell, Jonathan and Shepitsen, Andriy and Mobasher, Bamshad and Burke, Robin},
  file = {gemmell2008personalizing.pdf:gemmell2008personalizing.pdf:PDF},
  groups = {public},
  interhash = {e544ba095f411429896b11fd3f94fd5c},
  intrahash = {2e0535788c372e98e49646873cea4e1e},
  journal = {Data Warehousing and Knowledge Discovery},
  journalpub = {1},
  pages = {196--205},
  timestamp = {2009-08-10 10:30:08},
  title = {Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering},
  url = {http://dx.doi.org/10.1007/978-3-540-85836-2_19},
  username = {dbenz},
  year = 2008
}

@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
}