@article{journals/www/EdaYUU09,
  author = {Eda, Takeharu and Yoshikawa, Masatoshi and Uchiyama, Toshio and Uchiyama, Tadasu},
  ee = {http://dx.doi.org/10.1007/s11280-009-0069-1},
  interhash = {a560796c977bc7582017f662bf88c16d},
  intrahash = {ec3c256e7d1f24cd9d407d3ce7e41d96},
  journal = {World Wide Web},
  number = 4,
  pages = {421-440},
  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},
  volume = 12,
  year = 2009
}

@article{1413020,
  abstract = {We consider the problem of exploiting a taxonomy of propositionalized attributes in order to learn compact and robust classifiers. We introduce propositionalized attribute taxonomy guided decision tree learner (PAT-DTL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact decision trees. Since taxonomies are unavailable in most domains, we also introduce propositionalized attribute taxonomy learner (PAT-Learner) that automatically constructs taxonomy from data. PAT-DTL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the literals of decision rules from data and propositionalized attribute taxonomy. PAT-Learner propositionalizes attributes and hierarchically clusters the propositionalized attributes based on the distribution of class labels that co-occur with them to generate a taxonomy. Our experimental results on UCI repository data sets show that the proposed algorithms can generate a decision tree that is generally more compact than and is sometimes comparably accurate to those produced by standard decision tree learners.},
  address = {New York, NY, USA},
  author = {Kang, Dae-Ki and Sohn, Kiwook},
  doi = {http://dx.doi.org/10.1016/j.patcog.2008.07.009},
  interhash = {8c92a355c3401fac2c44b787ef8dd2ec},
  intrahash = {2238a6ae8a6d97a8835803d1bbcbb0d9},
  issn = {0031-3203},
  journal = {Pattern Recogn.},
  number = 1,
  pages = {84--92},
  publisher = {Elsevier Science Inc.},
  title = {Learning decision trees with taxonomy of propositionalized attributes},
  url = {http://portal.acm.org/citation.cfm?id=1413020},
  volume = 42,
  year = 2009
}

@techreport{citeulike:739394,
  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},
  citeulike-article-id = {739394},
  institution = {Computer Science Department},
  interhash = {d77846b40aadb0e25233cabf905bb93e},
  intrahash = {3b4ce6fd7fa6dbf1c39fd261fa39fcd6},
  month = {April},
  number = {2006-10},
  priority = {3},
  school = {Standford University},
  title = {Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems},
  url = {http://dbpubs.stanford.edu:8090/pub/2006-10},
  year = 2006
}

@inproceedings{conf/ausai/YooH03,
  author = {Yoo, Seung Yeol and Hoffmann, Achim G.},
  booktitle = {Australian Conference on Artificial Intelligence},
  ee = {http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2903&spage=65},
  interhash = {dedf28b26ccadc2c4e91db3d21140694},
  intrahash = {897fb5d2ab04ccbd613b9574fd4c9e47},
  pages = {65-76},
  title = {A New Approach for Concept-Based Web Search.},
  url = {http://dblp.uni-trier.de/db/conf/ausai/ausai2003.html#YooH03},
  year = 2003
}