@incollection{pol_introduction, author = {Lehmann, Jens and Voelker, Johanna}, booktitle = {Perspectives on Ontology Learning}, editor = {Lehmann, Jens and Voelker, Johanna}, interhash = {a53a9f1796f71f2f1c5ec646961f8924}, intrahash = {cf6a6785f5cab0525632a003c47ef5f7}, owner = {jl}, pages = {ix-xvi}, publisher = {AKA / IOS Press}, title = {An Introduction to Ontology Learning}, url = {http://jens-lehmann.org/files/2014/pol_introduction.pdf}, year = 2014 } @article{lohrmarch112013computer, author = {Lohr, Steve}, editor = {Times, New York}, interhash = {3ab18521dd9e04d43a64c93174104b93}, intrahash = {8d09e20df09a7df761eb23ca223d3391}, title = {NELL Is a Computer That Reads the Web - With a Little Human Help}, year = {March 11, 2013} } @book{manning2008, author = {Manning, Christopher D. and Raghavan, Prabhakar and Schütze, Hinrich}, interhash = {2e574e46b7668a7268e7f02b46f4d9bb}, intrahash = {9f4ab13e07b48b9723113aa74224be65}, publisher = {Cambridge University Press}, title = {Introduction to Information Retrieval}, year = 2008 } @inproceedings{jannach2014sixth, author = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, bibsource = {dblp computer science bibliography, http://dblp.org}, booktitle = {Eighth {ACM} Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, {USA} - October 06 - 10, 2014}, doi = {10.1145/2645710.2645786}, interhash = {b465a3695da123d6ee9de1675cb3d480}, intrahash = {5773f799bec72240eda5e6cfb6a03d7b}, pages = 395, title = {The sixth {ACM} RecSys workshop on recommender systems and the social web}, url = {http://doi.acm.org/10.1145/2645710.2645786}, year = 2014 } @book{manning2008introduction, abstract = {"Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures." -- Publisher's description.}, address = {New York}, author = {Manning, Christopher D. and Raghavan, Prabhakar and Schütze, Hinrich}, interhash = {2e574e46b7668a7268e7f02b46f4d9bb}, intrahash = {9f4ab13e07b48b9723113aa74224be65}, isbn = {9780521865715 0521865719}, publisher = {Cambridge University Press}, title = {Introduction to Information Retrieval}, url = {http://www.amazon.com/Introduction-Information-Retrieval-Christopher-Manning/dp/0521865719/ref=sr_1_1?ie=UTF8&qid=1337379279&sr=8-1}, year = 2008 } @book{jannach2011recommender, address = {New York}, author = {Jannach, Dietmar}, interhash = {a6610e8a27191994e0b85d73c26bdce8}, intrahash = {21abf93de5cf457f735f5beddec928a2}, isbn = {9780521493369 0521493366}, publisher = {Cambridge University Press}, refid = {645789647}, title = {Recommender systems : an introduction}, url = {http://www.amazon.de/Recommender-Systems-Introduction-Dietmar-Jannach/dp/0521493366/ref=sr_1_1?ie=UTF8&qid=1356099943&sr=8-1}, year = 2011 } @misc{sutton2010introduction, abstract = { Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling, combining the ability of graphical models to compactly model multivariate data with the ability of classification methods to perform prediction using large sets of input features. This tutorial describes conditional random fields, a popular probabilistic method for structured prediction. CRFs have seen wide application in natural language processing, computer vision, and bioinformatics. We describe methods for inference and parameter estimation for CRFs, including practical issues for implementing large scale CRFs. We do not assume previous knowledge of graphical modeling, so this tutorial is intended to be useful to practitioners in a wide variety of fields. }, author = {Sutton, Charles and McCallum, Andrew}, interhash = {05e1b6859124c5bf51c7aafd63f779b0}, intrahash = {49d8c9beb76a8b88739aa9eece7446ee}, note = {cite arxiv:1011.4088Comment: 90 pages}, title = {An Introduction to Conditional Random Fields}, url = {http://arxiv.org/abs/1011.4088}, year = 2010 } @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 } @book{davey1990introduction, address = {Cambridge}, author = {Davey, Brian A. and Priestley, Hilary A.}, interhash = {7255554003c02eb6ddf14a6fcb9b9f72}, intrahash = {df19796e33c1e2c861b613e3c8a86f58}, isbn = {0521365848 9780521365840 0521367662 9780521367660}, publisher = {Cambridge University Press}, refid = {471812885}, title = {Introduction to lattices and order}, url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=0521367662}, year = 1990 } @article{themenheft2007webmining, author = {Hotho, Andreas and Stumme, Gerd}, interhash = {39f94bf3a1663d9cec6a6cb8354a9bd9}, intrahash = {e9535ec82afa53f44a1b37704aa9a71f}, journal = {Künstliche Intelligenz}, number = 3, pages = {5-8}, title = {Mining the World Wide Web -- Methods, Ap- plications, and Perspectives}, url = {http://www.kuenstliche-intelligenz.de/index.php?id=7758}, year = 2007 } @proceedings{themenheft2007webmining, editor = {Hotho, Andreas and Stumme, Gerd}, interhash = {83c28b86f2ac897e906660e54e6fffc0}, intrahash = {c73311bb72ad480d74125dbc9d94c450}, journal = {Künstliche Intelligenz}, number = 3, pages = {5-8}, title = {Themenheft Web Mining, Künstliche Intelligenz}, url = {http://www.kuenstliche-intelligenz.de/index.php?id=7758}, year = 2007 } @article{berendt2010bridging, author = {Berendt, Bettina and Hotho, Andreas and Stumme, Gerd}, doi = {DOI: 10.1016/j.websem.2010.04.008}, interhash = {4969eb2b7bf1fabe60c5f23ab6383d77}, intrahash = {f8d7bc2af5753906dc3897196daac18c}, issn = {1570-8268}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, note = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences}, number = {2-3}, pages = {95 - 96}, title = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0}, url = {http://www.sciencedirect.com/science/article/B758F-4YXK4HW-1/2/4cb514565477c54160b5e6eb716c32d7}, volume = 8, year = 2010 } @article{citeulike:3782978, abstract = {The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for social signal processing ({SSP}) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially aware computing.}, address = {Newton, MA, USA}, author = {Vinciarelli, Alessandro and Pantic, Maja and Bourlard, Herv\'{e}}, citeulike-article-id = {3782978}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1621144.1621310}, citeulike-linkout-1 = {http://dx.doi.org/10.1016/j.imavis.2008.11.007}, citeulike-linkout-2 = {http://linkinghub.elsevier.com/retrieve/pii/S0262885608002485}, day = 03, doi = {10.1016/j.imavis.2008.11.007}, interhash = {639f01cfa95f7da21fd65afae66027a5}, intrahash = {0a4122235c6b50c760ff27aafc990999}, issn = {02628856}, journal = {Image and Vision Computing}, month = nov, number = 12, pages = {1743--1759}, posted-at = {2008-12-12 17:48:38}, priority = {2}, publisher = {Butterworth-Heinemann}, title = {Social signal processing: Survey of an emerging domain}, url = {http://dx.doi.org/10.1016/j.imavis.2008.11.007}, volume = 27, year = 2009 } @misc{Sutton2010, abstract = { Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling, combining the ability of graphical models to compactly model multivariate data with the ability of classification methods to perform prediction using large sets of input features. This tutorial describes conditional random fields, a popular probabilistic method for structured prediction. CRFs have seen wide application in natural language processing, computer vision, and bioinformatics. We describe methods for inference and parameter estimation for CRFs, including practical issues for implementing large scale CRFs. We do not assume previous knowledge of graphical modeling, so this tutorial is intended to be useful to practitioners in a wide variety of fields. }, author = {Sutton, Charles and McCallum, Andrew}, interhash = {05e1b6859124c5bf51c7aafd63f779b0}, intrahash = {49d8c9beb76a8b88739aa9eece7446ee}, note = {cite arxiv:1011.4088Comment: 90 pages}, title = {An Introduction to Conditional Random Fields}, url = {http://arxiv.org/abs/1011.4088}, year = 2010 } @phdthesis{library2010bibliometrics, abstract = {Bibliometrics: an overview • Research impact can be measured in many ways: quantitative approaches include publication counts, amount of research income, no of PhD students, size of research group, no of PI projects, views and downloads of online outputs, number of patents and licenses obtained, and others. • Use of bibliometrics and citation analysis is only one of these quantitative indicators. • The ability to apply it and its importance in the overall assessment of research varies from field to field • Attempts at quantitative measures can be contrasted with the main alternative assessment approach - qualitative peer-review in various forms • The balance between use of bibliometrics and peer-review in assessing academic performance at both the individual and unit levels is currently a “hot topic” being played out locally, nationally and internationally • This section provides an introductory overview of the field - others look in more depth at: the key uses of bibliometrics for journal ranking and individual assessment; the main metrics available; the main data sources and packaged toolkits available}, annote = {Kommentarö}, author = {LIBRARYä, UCD}, editor = {LIBRARY, UCD}, interhash = {0d2c15c908a2c9eb09edb5d2f943c735}, intrahash = {b34cd0d454229e69758efea1df97a68b}, note = {Hinweisä}, publisher = {Springer Verlag}, title = {2Bibliometrics - an introduction}, url = {http://www.ucd.ie/library/guides/pdf/bibliometrics/Bibliometrics.pdf}, year = 2010 } @article{opinion.review.2010, author = {Liu, Bing}, editor = {Indurkhya, N. and Damerau, F. J.}, interhash = {d95273d3139fee537a2e08a2fc5b4b38}, intrahash = {098caba4af7a344db7a62c5d10748727}, journal = {Handbook of Natural Language Processing}, title = {Sentiment Analysis and Subjectivity}, volume = {2nd ed}, year = 2010 } @article{berendt2010bridging, author = {Berendt, Bettina and Hotho, Andreas and Stumme, Gerd}, doi = {DOI: 10.1016/j.websem.2010.04.008}, interhash = {4969eb2b7bf1fabe60c5f23ab6383d77}, intrahash = {f8d7bc2af5753906dc3897196daac18c}, issn = {1570-8268}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, note = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences}, number = {2-3}, pages = {95 - 96}, title = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0}, url = {http://www.sciencedirect.com/science/article/B758F-4YXK4HW-1/2/4cb514565477c54160b5e6eb716c32d7}, volume = 8, year = 2010 } @article{Berendt201095, author = {Berendt, Bettina and Hotho, Andreas and Stumme, Gerd}, doi = {DOI: 10.1016/j.websem.2010.04.008}, interhash = {4969eb2b7bf1fabe60c5f23ab6383d77}, intrahash = {f8d7bc2af5753906dc3897196daac18c}, issn = {1570-8268}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, note = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences}, number = {2-3}, pages = {95 - 96}, title = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0}, url = {http://www.sciencedirect.com/science/article/B758F-4YXK4HW-1/2/4cb514565477c54160b5e6eb716c32d7}, volume = 8, year = 2010 } @article{New03, author = {Newman, M. E. J.}, interhash = {7bedd01cb4c06af9f5200b0fb3faa571}, intrahash = {f0de28071b8ee1c3675e67c7538e806a}, journal = {SIAM Review}, number = 2, pages = {167-256}, title = {The structure and function of complex networks}, volume = 45, year = 2003 } @unpublished{butler2006-3, author = {Butler, Steve}, interhash = {852cf90cdc865cd9c7985875bcde2160}, intrahash = {08749179a19f8bc991ed31a5cd75d386}, title = {Spectral Graph Theory: Cheeger constants and discrepancy}, year = 2006 }