@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
}

@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{hjelm2008multilingual,
  abstract = {We present a system for taxonomy extraction, aimed at providing a taxonomic backbone in an ontology learning environment. We follow previous research in using hierarchical clustering based on distributional similarity of the terms in texts. We show that basing the clustering on a comparable corpus in four languages gives a considerable improvement in accuracy compared to using only the monolingual English texts. We also show that hierarchical k-means clustering increases the similarity to the original taxonomy, when compared with a bottom-up agglomerative clustering approach.},
  author = {Hjelm, Hans and Buitelaar, Paul},
  booktitle = {ECAI},
  crossref = {conf/ecai/2008},
  editor = {Ghallab, Malik and Spyropoulos, Constantine D. and Fakotakis, Nikos and Avouris, Nikolaos M.},
  ee = {http://dx.doi.org/10.3233/978-1-58603-891-5-288},
  file = {hjelm2008multilingual.pdf:hjelm2008multilingual.pdf:PDF},
  groups = {public},
  interhash = {21a658154fb1a02e773b7a678b15f9f4},
  intrahash = {813903a333a40ecf9a59ded552acb323},
  isbn = {978-1-58603-891-5},
  pages = {288-292},
  publisher = {IOS Press},
  series = {Frontiers in Artificial Intelligence and Applications},
  timestamp = {2011-01-18 12:06:01},
  title = {Multilingual Evidence Improves Clustering-based Taxonomy Extraction.},
  url = {http://www.ling.su.se/staff/hans/artiklar/ecai2008-hjelm-buitelaar.pdf},
  username = {dbenz},
  volume = 178,
  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
}

@inproceedings{snow2006semantic,
  abstract = {We propose a novel algorithm for inducing semantic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word’s coordinate terms to help in determining its hypernyms, and vice versa. We apply our algorithm on the problem of sense-disambiguated noun hyponym acquisition, where we combine the predictions of hypernym and coordinate term classifiers with the knowledge in a preexisting semantic taxonomy (WordNet 2.1). We add 10; 000 novel synsets to WordNet 2.1 at 84% precision, a relative error reduction of 70% over a non-joint algorithm using the same component classifiers. Finally, we show that a taxonomy built using our algorithm shows a 23% relative F-score improvement over WordNet 2.1 on an independent testset of hypernym pairs.},
  author = {Snow, Rion and Jurafsky, Daniel and Ng, Andrew Y.},
  booktitle = {ACL},
  crossref = {conf/acl/2006},
  ee = {http://acl.ldc.upenn.edu/P/P06/P06-1101.pdf},
  file = {snow2006semantic.pdf:snow2006semantic.pdf:PDF},
  groups = {public},
  interhash = {c0f5a3a22faa8dc4b61c9a717a6c9037},
  intrahash = {8f39e7ac43a97719c5a746da02dbd964},
  publisher = {The Association for Computer Linguistics},
  timestamp = {2010-10-25 15:06:10},
  title = {Semantic Taxonomy Induction from Heterogenous Evidence.},
  url = {http://dblp.uni-trier.de/db/conf/acl/acl2006.html#SnowJN06},
  username = {dbenz},
  year = 2006
}