@proceedings{30474,
  author = {Tresp, Volker and Bundschus, Markus and Rettinger, Achim and Huang, Yi},
  interhash = {e27fbf5b5fb16f66cd0c7a3932fc4695},
  intrahash = {006468688804bc3563225b8dcd7aea97},
  journal = {Uncertainty Reasoning for the Semantic Web I Lecture Notes in AI},
  publisher = {Springer},
  title = {Towards machine learning on the semantic web},
  url = {http://wwwbrauer.informatik.tu-muenchen.de/~trespvol/papers/LearningRDF23.pdf},
  year = 2008
}

@article{kosala00web,
  address = {New York, NY, USA},
  author = {Kosala, R. and Blockeel, H.},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  interhash = {99eea914954da48c9691277ce4e32932},
  intrahash = {59f6ef686827c7095cc89ebdb056a222},
  journal = {SIGKDD Explorations},
  number = 1,
  pages = {1--15},
  publisher = {ACM},
  title = {Web Mining Research: {A} Survey},
  url = {http://citeseer.nj.nec.com/kosala00web.html},
  volume = 2,
  year = 2000
}

@proceedings{Staab2004HOO,
  address = {Berlin; New York},
  booktitle = {International handbooks on information systems},
  editor = {Staab, Steffen and Studer, Rudi},
  interhash = {494a7427b9dd11496d824c824b35938b},
  intrahash = {f920f0aabbe7a02c9fe5d65c298bc8ea},
  issn = {3540408347  9783540408345},
  pages = {--},
  publisher = {Springer},
  refid = {53814725},
  title = {Handbook on ontologies},
  year = 2004
}

@article{citeulike:2146554,
  abstract = {Nowadays, multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification. This paper introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multi-label classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.},
  author = {Tsoumakas, G. and Katakis, I.},
  citeulike-article-id = {2146554},
  editor = {Taniar, David},
  interhash = {f8e6c4b6b3df7461d070a1a9cc1d15c1},
  intrahash = {52c3b18481f5146e4c213d609c1143fc},
  journal = {International Journal of Data Warehouse and Mining},
  number = 3,
  pages = {1--13},
  posted-at = {2007-12-19 13:38:29},
  priority = {2},
  publisher = {Idea Group Publishing},
  title = {Multi Label Classification: An Overview},
  volume = 3,
  year = 2007
}

@article{Famili:1997:1088-467X:3,
  abstract = {This paper first provides an overview of data preprocessing, focusing on problems of real world data. These are primarily problems that have to be carefully understood and solved before any data analysis process can start. The paper discusses in detail two main reasons for performing data preprocessing: (i) problems with the data and (ii) preparation for data analysis. The paper continues with details of data preprocessing techniques achieving each of the above mentioned objectives. A total of 14 techniques are discussed. Two examples of data preprocessing applications from two of the most data rich domains are given at the end. The applications are related to semiconductor manufacturing and aerospace domains where large amounts of data are available, and they are fairly reliable. Future directions and some challenges are discussed at the end.},
  author = {Famili, A. and Shen, W.-M. and Weber, R. and Simoudis, E.},
  doi = {doi:10.1016/S1088-467X(98)00007-9},
  interhash = {3da26163f9537a42a984f7bfb8456fd3},
  intrahash = {808497c457a1d53d78b5455f0ed71912},
  journal = {Intelligent Data Analysis},
  pages = {3-23(21)},
  title = {Data Preprocessing and Intelligent Data Analysis},
  url = {http://www.iit-iti.nrc-cnrc.gc.ca/publications/nrc-40166_e.html},
  volume = 1,
  year = 1997
}

@article{survey91safavian,
  abstract = {A survey is presented of current methods for decision tree classifier (DTC) designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, the subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed. The relation between decision trees and neutral networks (NN) is also discussed},
  author = {Safavian, S. R. and Landgrebe, D.},
  booktitle = {Systems, Man and Cybernetics, IEEE Transactions on},
  interhash = {d191b7a5dd9037f7e05357e9be3cf1c2},
  intrahash = {348c3ca0090e508133fffdf656b2432a},
  journal = {Systems, Man and Cybernetics, IEEE Transactions on},
  number = 3,
  pages = {660--674},
  title = {A survey of decision tree classifier methodology},
  url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=97458},
  volume = 21,
  year = 1991
}

@article{Burke02,
  author = {Burke, Robin},
  file = {Burke02.pdf:Burke02.pdf:PDF},
  interhash = {f40020400b8bc08adca29a987caf25d8},
  intrahash = {2dd27925f83ea7b04b5fc444938b866b},
  journal = {User Modeling and User Adapted Interaction},
  number = 4,
  owner = {stormerh},
  pages = {331-370},
  timestamp = {2006.11.20},
  title = {Hybrid Recommender Systems, Survey and Experiments},
  volume = 12,
  year = 2002
}

@article{keyhere,
  abstract = {Abstract  Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing
and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeksto discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneckof ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the pastdecade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion ofmajor issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology developmentand a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learningapproaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domaincharacteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insightsabout this fast-growing field.},
  author = {Zhou, Lina},
  interhash = {78b6d3db998dcd27c475dfff3816f48f},
  intrahash = {95b0f4f7c9c628e032d8bb4c69b432ed},
  journal = {Information Technology and Management},
  month = {#sep#},
  number = 3,
  pages = {241--252},
  title = {Ontology learning: state of the art and open issues},
  url = {http://dx.doi.org/10.1007/s10799-007-0019-5},
  volume = 8,
  year = 2007
}

@article{4686305,
  abstract = {Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations : tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This article compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web.},
  author = {Limpens, Freddy and Gandon, Fabien and Buffa, Michel},
  doi = {10.1109/ASEW.2008.4686305},
  interhash = {cb1d534be80d664a50df66e8977b774e},
  intrahash = {9372f9c2db8b9f4cf05b3db84e6589ac},
  journal = {Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on},
  month = {Sept.},
  pages = {13-18},
  title = {Bridging ontologies and folksonomies to leverage knowledge sharing on the social Web: A brief survey},
  year = 2008
}

@article{1282,
  author = {Cimiano, Philipp and Völker, Johanna and Studer, Rudi},
  interhash = {aeb553dc2e190f0a5974dfdc709d450a},
  intrahash = {fe4c2950b5be221b493e29e4339240e8},
  journal = {Information, Wissenschaft und Praxis},
  month = OCT,
  note = {see the special issue for more contributions related to the Semantic Web},
  number = {6-7},
  pages = {315-320},
  title = {Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text},
  url = {\url{http://www.aifb.uni-karlsruhe.de/WBS/pci/Publications/iwp06.pdf}},
  volume = 57,
  year = 2006
}