@incollection{niebler2013tagging,
  abstract = {The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which },
  author = {Niebler, Thomas and Singer, Philipp and Benz, Dominik and Körner, Christian and Strohmaier, Markus and Hotho, Andreas},
  booktitle = {Advances in Information Retrieval},
  doi = {10.1007/978-3-642-36973-5_8},
  editor = {Serdyukov, Pavel and Braslavski, Pavel and Kuznetsov, SergeiO. and Kamps, Jaap and Rüger, Stefan and Agichtein, Eugene and Segalovich, Ilya and Yilmaz, Emine},
  interhash = {8f11f2140d9eb369a7ca42cd527f76c1},
  intrahash = {8583743a7598e78cc7b4e8af71a43902},
  isbn = {978-3-642-36972-8},
  pages = {86-97},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  title = {How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-36973-5_8},
  volume = 7814,
  year = 2013
}

@article{springerlink:10.1007/s10115-010-0356-2,
  abstract = {Subgroup discovery is a data mining technique which extracts interesting rules with respect to a target variable. An important characteristic of this task is the combination of predictive and descriptive induction. An overview related to the task of subgroup discovery is presented. This review focuses on the foundations, algorithms, and advanced studies together with the applications of subgroup discovery presented throughout the specialised bibliography.},
  affiliation = {Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain},
  author = {Herrera, Franciso and Carmona, Cristóbal and González, Pedro and del Jesus, María},
  doi = {10.1007/s10115-010-0356-2},
  interhash = {54d81a413473b482266e009d272c319a},
  intrahash = {3cef7c3a62fcc6ae55753570bd041f5e},
  issn = {0219-1377},
  journal = {Knowledge and Information Systems},
  keyword = {Computer Science},
  pages = {1-31},
  publisher = {Springer London},
  title = {An overview on subgroup discovery: foundations and applications},
  url = {http://dx.doi.org/10.1007/s10115-010-0356-2},
  year = 2010
}

@incollection{jaeschke2012challenges,
  abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.},
  address = {Berlin/Heidelberg},
  affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany},
  author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {Recommender Systems for the Social Web},
  doi = {10.1007/978-3-642-25694-3_3},
  editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.},
  interhash = {75b1a6f54ef54d0126d0616b5bf77563},
  intrahash = {7d41d332cccc3e7ba8e7dadfb7996337},
  isbn = {978-3-642-25694-3},
  pages = {65--87},
  publisher = {Springer},
  series = {Intelligent Systems Reference Library},
  title = {Challenges in Tag Recommendations for Collaborative Tagging Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3},
  volume = 32,
  year = 2012
}

@inproceedings{ADHMS:11,
  author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011},
  interhash = {49e97def917e352ca21ab2e3eb7bd88a},
  intrahash = {1fe037ea2712b205c564243d67840059},
  title = {Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players},
  year = 2011
}

@incollection{Fayyad:1996:DMK:257938.257942,
  acmid = {257942},
  address = {Menlo Park, CA, USA},
  author = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic},
  chapter = {From data mining to knowledge discovery: an overview},
  editor = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic and Uthurusamy, Ramasamy},
  interhash = {e62d85a492bbc917f43a5d9c8b775189},
  intrahash = {d0b54b224b992e51d892d0f06d45cf6b},
  isbn = {0-262-56097-6},
  numpages = {34},
  pages = {1--34},
  publisher = {American Association for Artificial Intelligence},
  title = {Advances in knowledge discovery and data mining},
  url = {http://portal.acm.org/citation.cfm?id=257938.257942},
  year = 1996
}

@inproceedings{atze09,
  address = {Krakow, Poland},
  author = {Atzmueller, Martin and Lemmerich, Florian and Krause, Beate and Hotho, Andreas},
  booktitle = {7th Conference on Computer Methods and Systems},
  interhash = {c226a55c0cc2dc6f261b86c09225c260},
  intrahash = {014dbd07807e05a5ea9aafb2dbead39b},
  month = {November},
  note = {ISBN 83-916420-5-4},
  title = {Who are the Spammers? Understandable Local Patterns for Concept Description},
  url = {http://www.cms.agh.edu.pl/},
  year = 2009
}

@inproceedings{ALKH:09,
  author = {Atzmueller, Martin and Lemmerich, Florian and Krause, Beate and Hotho, Andreas},
  booktitle = {Proc. LeGo-09: From Local Patterns to Global Models, Workshop at the 2009 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  editor = {Knobbe, Johannes F\"urnkranz Arno},
  interhash = {d27cd7eee4ab571ad3753a3d370141ce},
  intrahash = {bb80bdcc06c8886968c453fd920dfe05},
  note = {accepted},
  title = {{Towards Understanding Spammers - Discovering Local Patterns for Concept Characterization and Description}},
  url = {http://www.ke.tu-darmstadt.de/events/LeGo-09/04-Atzmueller.pdf},
  year = 2009
}

@book{hotho2008challenge,
  editor = {Hotho, Andreas and Benz, Dominik and Jäschke, Robert and Krause, Beate},
  interhash = {fbcdc431904808bb868f09734b91af87},
  intrahash = {1d5d5ef0bb222cb2f3adef4d6b06f1ea},
  publisher = {Workshop at 18th Europ. Conf. on Machine Learning (ECML'08) / 11th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'08)},
  title = {ECML PKDD Discovery Challenge 2008 (RSDC'08)},
  url = {http://www.kde.cs.uni-kassel.de/ws/rsdc08/pdf/all_rsdc_v2.pdf},
  year = 2008
}

@article{kostoff,
  abstract = {Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. This paper describes the second medical problem we addressed (cataract) using ODS LRD; the first problem addressed was Raynaud's Phenomenon (RP), and was described in the third paper of this Special Issue. Cataract was selected because it is ubiquitous globally, appears intractable to all forms of treatment other than surgical removal of cataracts, and is a major cause of blindness in many developing countries. The ODS LRD study had three objectives: a) identify non-drug non-surgical treatments that would 1) help prevent cataracts, or 2) reduce the progression rate of cataracts, or 3) stop the progression of cataracts, or 4) maybe even reverse the progression of cataracts; b) demonstrate that we could solve an ODS LRD problem with no prior knowledge of any results or prior work (unlike the case with the RP problem); c) determine whether large time savings in the discovery process were possible relative to the time required for conducting the RP study. To that end, we used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, as a substitute for the manually-intensive process used in the RP study to restrict potential discoveries to selected semantic classes. We also used additional semantic filtering to identify potential discovery within the selected semantic classes. All these goals were achieved. As will be shown, we generated large amounts of potential discovery in more than an order of magnitude less time than required for the RP study. We identified many non-drug non-surgical treatments that may be able to reduce or even stop the progression rate of cataracts. Time, and much testing, will determine whether this is possible. Finally, the methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.},
  author = {Kostoff, Ronald N.},
  interhash = {45ce0cd73dd62182ce1e447ba9fe71eb},
  intrahash = {b9359f79985da9b9677340ffda849e74},
  journal = {Technological Forecasting and Social Change},
  pages = {--},
  title = {Literature-related discovery (LRD): Potential treatments for cataracts},
  url = {http://www.sciencedirect.com/science/article/B6V71-4RDB8SC-9/2/8991fe8968a0ef12f22ed7e9ac9d7c4f},
  volume = {In Press, Corrected Proof},
  year = 2007
}

@book{books/mit/FayyadPSU96,
  editor = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic and Uthurusamy, Ramasamy},
  interhash = {c11811ccd720de5dad0ffea4741725f0},
  intrahash = {3553c3acc971c03813352c40afe7476a},
  isbn = {0-262-56097-6},
  publisher = {AAAI/MIT Press},
  title = {Advances in Knowledge Discovery and Data Mining.},
  url = {http://www.amazon.com/gp/product/0262560976},
  year = 1996
}

@inproceedings{Buchanan01,
  address = {New York},
  author = {Buchanan, B.},
  booktitle = {KDD 2000 -- Proceedings of the Sixth ACM SIGKDD               International Conference on Knowledge Discovery and Data               Mining, Boston, MA, August 20-23, 2000},
  interhash = {f3eea6ac2e67b417ff569c65f2ab9bb4},
  intrahash = {0059da2018c3343855c2fa45d4fac248},
  isbn = {3-540-43760-6},
  pages = 3,
  publisher = {ACM},
  title = {Informed Knowledge Discovery: Using Prior Knowledge in Discovery Programs},
  year = 2000
}

@article{DT99,
  author = {Dehaspe, L. and Toivonen, H.},
  interhash = {eb17bc1d3cea0dc35d4875bd5386a5d7},
  intrahash = {53fa388ae74f5dec8c8fb89b66006fed},
  journal = {Data Mining and Knowledge Discovery},
  location = {Santa Barbara, CA},
  number = 1,
  pages = {7--36},
  title = {Discovery of Frequent Datalog Patterns},
  volume = 3,
  year = 1999
}

@article{Srivastavaetal,
  author = {Srivastava, J. and Cooley, R. and Deshpande, M. and Tan, P.-N.},
  interhash = {08571943908ec1aa9aa5c003e79d5b8d},
  intrahash = {dc941da0f5c7da937269241b0df0b3b3},
  journal = {SIGKDD Explorations},
  location = {Santa Barbara, CA},
  number = 2,
  pages = {12--23},
  title = {Web usage mining: discovery and application of usage patterns from web data},
  url = {http://citeseer.nj.nec.com/srivastava00web.html},
  volume = 1,
  year = 2000
}

@incollection{Cooleyetal00,
  author = {Cooley, R. and Tang, P.-N. and Srivastava, J.},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {\cite{webkdd99book}},
  interhash = {4cb524eff321348696985af1d63a3e59},
  intrahash = {5862558933e364680abc29bd5b4e0d84},
  pages = {163--182},
  title = {Discovery of interesting usage patterns from web data},
  year = 2000
}

@inproceedings{Chietal02,
  author = {Chi, E.H. and Rosien, A. and Heer, J.},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {\cite{webkdd02}},
  interhash = {1b9ed5289bc9d1cccc49b088acdbd218},
  intrahash = {74fc583dc7492a2c51e78b0d7a522fe9},
  pages = {1--15},
  title = {Intelligent discovery and analysis of web user traffic composition},
  year = 2002
}

@techreport{Zaiane98,
  author = {Zaïane, Osmar R.},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  institution = {Simon Fraser University},
  interhash = {0a8bfd000667c1f72550e48d7ab7e055},
  intrahash = {d1cea509405cb48d2fb6d4ce22e480e1},
  number = {TR 1998-13},
  title = {From Resource Discovery to Knowledge Discovery on the Internet},
  type = {Technical Report},
  url = {citeseer.nj.nec.com/117999.html},
  year = 1998
}