@misc{ailon-2007, title = {An efficient reduction of ranking to classification}, author = {Nir Ailon and Mehryar Mohri}, year = 2007, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0710.2889}, description = {[0710.2889] An efficient reduction of ranking to classification}, abstract = { This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction guarantees an average pairwise misranking regret of at most that of the binary classifier regret, improving a recent result of Balcan et al which only guarantees a factor of 2. Moreover, our reduction applies to a broader class of ranking loss functions, admits a simpler proof, and the expected running time complexity of our algorithm in terms of number of calls to a classifier or preference function is improved from $\Omega(n^2)$ to $O(n \log n)$. In addition, when the top $k$ ranked elements only are required ($k \ll n$), as in many applications in information extraction or search engines, the time complexity of our algorithm can be further reduced to $O(k \log k + n)$. Our reduction and algorithm are thus practical for realistic applications where the number of points to rank exceeds several thousands. Much of our results also extend beyond the bipartite case previously studied.}, biburl = {http://www.bibsonomy.org/bibtex/2d8bd1b99e3c245d17b577514727ebff2/hotho}, keywords = {learning toread ranking} } @inproceedings{nldb05, title = {Text2Onto - A Framework for Ontology Learning and Data-driven Change Discovery}, address = {Alicante, Spain}, author = {Philipp Cimiano and Johanna Völker}, booktitle = {Proceedings of the 10th International Conference on Applications of Natural Language to Information Systems (NLDB)}, editor = {Andres Montoyo and Rafael Munoz and Elisabeth Metais}, month = {JUN}, pages = {227-238}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = 3513, year = 2005, url = {\url{http://www.aifb.uni-karlsruhe.de/WBS/jvo/publications/Text2Onto_nldb_2005.pdf}}, description = {Institut AIFB - Publikation: Text2Onto - A Framework for Ontology Learning and Data-driven Change Discovery}, biburl = {http://www.bibsonomy.org/bibtex/2072436e5adc4f5fdc39f4baeaa55b077/hotho}, keywords = {text2onto ol ontology kaon learning nlp} } @article{1282, title = {Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text}, author = {Philipp Cimiano and Johanna Völker and Rudi Studer}, 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}, volume = 57, year = 2006, url = {\url{http://www.aifb.uni-karlsruhe.de/WBS/pci/Publications/iwp06.pdf}}, description = {Institut AIFB - Publikation: Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text}, biburl = {http://www.bibsonomy.org/bibtex/2fe4c2950b5be221b493e29e4339240e8/hotho}, keywords = {ontology survey learning toread} } @misc{cattuto-2008, title = {Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems}, author = {Ciro Cattuto and Dominik Benz and Andreas Hotho and Gerd Stumme}, year = 2008, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0805.2045}, description = {[0805.2045] Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems}, abstract = { Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.}, biburl = {http://www.bibsonomy.org/bibtex/278fd64c3db55e6387ebdeb6c40054542/hotho}, keywords = {semantic learning similarity analysis ol tag myown 2008 ontology} } @article{charniak97statistical, title = {Statistical Techniques for Natural Language Parsing}, author = {Eugene Charniak}, journal = {AI Magazine}, number = 4, pages = {33-44}, volume = 18, year = 1997, url = {http://citeseer.ist.psu.edu/article/charniak97statistical.html}, biburl = {http://www.bibsonomy.org/bibtex/21d02e8f9d663f5cd8203ec6685a958ed/hotho}, keywords = {lecture tagging model nlp learning} } @article{cimiano2005learning, title = {Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis}, author = {P. Cimiano and A. Hotho and S. Staab}, journal = {Journal of Artificial Intelligence Research (JAIR)}, pages = {305-339}, publisher = {AAAI Press}, volume = 24, year = 2005, url = {http://www.jair.org/media/1648/live-1648-2403-jair.pdf}, issn = {1076-9757}, vgwort = {54}, biburl = {http://www.bibsonomy.org/bibtex/22d7c9ea5484ee45ea8bf3520138d7477/hotho}, keywords = {myown SumSchool06 learning text ol fca 2005} } @inproceedings{Benz07OL, title = {Position Paper: Ontology Learning from Folksonomies.}, author = {Dominik Benz and Andreas Hotho}, booktitle = {LWA 2007: Lernen - Wissen - Adaption, Halle, September 2007, Workshop Proceedings (LWA)}, crossref = {conf/lwa/2007}, editor = {Alexander Hinneburg}, pages = {109-112}, publisher = {Martin-Luther-University Halle-Wittenberg}, year = 2007, url = {http://dblp.uni-trier.de/db/conf/lwa/lwa2007.html#BenzH07}, isbn = {978-3-86010-907-6}, vgwort = {16}, date = {2007-11-16}, description = {dblp}, biburl = {http://www.bibsonomy.org/bibtex/2ad31989b2393f5d0c4e8be8dbb613141/hotho}, keywords = {summerschool ontology kdubiq learning myown 2007 folksonomy} } @inproceedings{voelker1:07:eswc, title = {{Learning Disjointness}}, author = {Johanna Völker and Denny Vrandecic and York Sure and Andreas Hotho}, booktitle = {Proceedings of the European Semantic Web Conference, ESWC2007}, editor = {Enrico Franconi and Michael Kifer and Wolfgang May}, month = {July}, publisher = {Springer-Verlag}, series = {Lecture Notes in Computer Science}, volume = 4519, year = 2007, url = {http://www.eswc2007.org/pdf/eswc07-voelker1.pdf}, vgwort = {26}, biburl = {http://www.bibsonomy.org/bibtex/2c5c43ae4a719e6e935a9ca1a4aca906b/hotho}, keywords = {ol myown eswc learning 2007 ontology} } @article{heylighen98bootstrapping, title = {Bootstrapping knowledge representations: from entailment meshes via semantic nets to learning webs}, author = {Francis Heylighen}, journal = {Kybernetes}, number = {5/6}, pages = {691--722}, volume = 30, year = 2001, url = {citeseer.nj.nec.com/francis96bootstrapping.html}, comment = {meh: general ontology, knowledge structures}, biburl = {http://www.bibsonomy.org/bibtex/22fe2537d42bd46b0a31a25c125eb865d/hotho}, keywords = {kdubiq ontology summerschool learning folksonomy semantic} } @techreport{cimianpo_hotho_staab_OL_FCA_04, title = {Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis}, author = {Philipp Cimiano and Andreas Hotho and Steffen Staab}, institution = {Institute AIFB, Universität Karlsruhe}, month = {NOV}, year = 2004, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2004/techOntologylearningFCA.pdf}, file = {}, biburl = {http://www.bibsonomy.org/bibtex/2c46361a68b3dd167a288ba05a74a88bb/hotho}, keywords = {learning hierarchies 2004 fca myown} } @inproceedings{cim04c, title = {Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text}, address = {Valencia, Spain}, author = {Philipp Cimiano and Andreas Hotho and Steffen Staab}, booktitle = {Proceedings of the European Conference on Artificial Intelligence (ECAI'04)}, editor = {Ramon L{\'o}pez de M{\'a}ntaras and Lorenza Saitta}, pages = {435-439}, publisher = {IOS Press}, year = 2004, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2004/ecai04.pdf}, file = {}, isbn = {1-58603-452-9}, biburl = {http://www.bibsonomy.org/bibtex/248d35aa9a4d727e221c90f959462b7b2/hotho}, keywords = {myown 2004 learning taxonomies clustering} } @article{keyhere, title = {kNN Versus SVM in the Collaborative Filtering Framework}, author = {Miha Grčar and Blaž Fortuna and Dunja Mladenič and Marko Grobelnik}, journal = {Data Science and Classification}, pages = {251--260}, year = 2006, url = {http://db.cs.ualberta.ca/webkdd05/proc/paper25-mladenic.pdf}, doi = {http://dx.doi.org/10.1007/3-540-34416-0_27}, description = {SpringerLink - Book Chapter}, abstract = {We present experimental results of confronting the k-Nearest Neighbor (kNN) algorithm with Support Vector Machine (SVM) in the collaborative filtering framework using datasets with different properties. While k-Nearest Neighbor is usually used forthe collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algorithm. Sincecollaborative filtering can also be interpreted as a classification/regression task, virtually any supervised learning algorithm(such as SVM) can also be applied. Experiments were performed on two standard, publicly available datasets and, on the otherhand, on a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering. We concludethat the quality of collaborative filtering recommendations is highly dependent on the quality of the data. Furthermore, wecan see that kNN is dominant over SVM on the two standard datasets. On the real-life corporate dataset with high level ofsparsity, kNN fails as it is unable to form reliable neighborhoods. In this case SVM outperforms kNN.}, biburl = {http://www.bibsonomy.org/bibtex/249c80c0aeb3c7eeb1941dcb62a5d26f3/hotho}, keywords = {svm knn learning recommender} } @inproceedings{658298, title = {Collaborative Learning and Recommender Systems}, address = {San Francisco, CA, USA}, author = {Wee Sun Lee}, booktitle = {ICML '01: Proceedings of the Eighteenth International Conference on Machine Learning}, pages = {314--321}, publisher = {Morgan Kaufmann Publishers Inc.}, year = 2001, url = {http://www.comp.nus.edu.sg/~leews/publications/icml01.pdf}, isbn = {1-55860-778-1}, description = {Collaborative Learning and Recommender Systems}, biburl = {http://www.bibsonomy.org/bibtex/2cf5ddf4740a73d8c161e704cac3240f6/hotho}, keywords = {recommender learning classification} } @inproceedings{657311, title = {Learning Collaborative Information Filters}, address = {San Francisco, CA, USA}, author = {Daniel Billsus and Michael J. Pazzani}, booktitle = {ICML '98: Proceedings of the Fifteenth International Conference on Machine Learning}, pages = {46--54}, publisher = {Morgan Kaufmann Publishers Inc.}, year = 1998, url = {http://www.ics.uci.edu/~pazzani/Publications/MLC98.pdf}, isbn = {1-55860-556-8}, description = {Learning Collaborative Information Filters}, biburl = {http://www.bibsonomy.org/bibtex/2977851e8e6cb73b8b94b0cea69dbb9e3/hotho}, keywords = {classification learning recommender} } @article{rashid2005irb, title = {{Influence in ratings-based recommender systems: An algorithm-independent approach}}, author = {A. M. Rashid and G. Karypis and J. Riedl}, journal = {Proceedings of the SIAM International Conference on Data Mining}, year = 2005, url = {http://www.grouplens.org/papers/pdf/RashidAl_siam05.pdf}, biburl = {http://www.bibsonomy.org/bibtex/265e6a92489e5190ca5129532d2d138fd/hotho}, keywords = {toread learning recommender} } @inproceedings{conf/icml/JensenN02, title = {Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning.}, author = {David Jensen and Jennifer Neville}, booktitle = {ICML}, crossref = {conf/icml/2002}, editor = {Claude Sammut and Achim G. Hoffmann}, pages = {259-266}, publisher = {Morgan Kaufmann}, year = 2002, url = {http://dblp.uni-trier.de/db/conf/icml/icml2002.html#JensenN02}, isbn = {1-55860-873-7}, date = {2002-11-22}, description = {dblp}, biburl = {http://www.bibsonomy.org/bibtex/2323836c3a8a82637e562cada59ccb6be/hotho}, keywords = {relational learning toread autocorrelation} } @book{thrun2001, title = {Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)}, author = {Sebastian Thrun and Wolfram Burgard and Dieter Fox}, year = 2001, url = {http://www.amazon.com/Probabilistic-Robotics-Intelligent-Autonomous-Agents/dp/0262201623/ref=sr_11_1/105-3361811-4085215?ie=UTF8&qid=1190743235&sr=11-1}, typesource = {Simple CitationSource}, source = {}, asin = {0262201623}, pubmed = {}, isdn = {978-0-262-20162-9}, doi = {}, description = {Amazon.com: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents): Books: Sebastian Thrun,Wolfram Burgard,Dieter Fox}, biburl = {http://www.bibsonomy.org/bibtex/2914a56f048c863f0928bb6d1efe09ff7/hotho}, keywords = {ml machine dm probabilistic learning} } @techreport{zhu05survey, title = {Semi-Supervised Learning Literature Survey}, author = {Xiaojin Zhu}, institution = {Computer Sciences, University of Wisconsin-Madison}, number = 1530, year = 2005, url = {http://pages.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf}, biburl = {http://www.bibsonomy.org/bibtex/2f30e96810896e4cad560e59e3c24730f/hotho}, keywords = {supervised semi toread clustering learning} } @inproceedings{Kohavi95, title = {A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection}, author = {Ron Kohavi}, booktitle = {Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence}, pages = {1137-1145}, publisher = {San Mateo, CA: Morgan Kaufmann}, year = 1995, description = {WSD}, biburl = {http://www.bibsonomy.org/bibtex/27389c34380588234cb69bd5dfdb6bd2f/hotho}, keywords = {learning cross validation} } @book{buitelaar05ontologylearningbook, title = {Ontology Learning from Text: Methods, Evaluation and Applications}, editor = {Paul Buitelaar and Philipp Cimiano and Bernardo Magnini}, month = {JUL}, publisher = {IOS Press}, series = {Frontiers in Artificial Intelligence}, volume = 123, year = 2005, biburl = {http://www.bibsonomy.org/bibtex/20e71ddd52894af0e681b9d9411f7944f/hotho}, keywords = {semantic ol web learning ml dm} }