@article{mnih2015humanlevel,
  author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A. and Veness, Joel and Bellemare, Marc G. and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K. and Ostrovski, Georg and Petersen, Stig and Beattie, Charles and Sadik, Amir and Antonoglou, Ioannis and King, Helen and Kumaran, Dharshan and Wierstra, Daan and Legg, Shane and Hassabis, Demis},
  interhash = {eac59980357d99db87b341b61ef6645f},
  intrahash = {fb15f4471c81dc2b9edf2304cb2f7083},
  issn = {00280836},
  journal = {Nature},
  month = feb,
  number = 7540,
  pages = {529--533},
  publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
  title = {Human-level control through deep reinforcement learning},
  url = {http://dx.doi.org/10.1038/nature14236},
  volume = 518,
  year = 2015
}

@article{kluegl2013exploiting,
  abstract = {Conditional Random Fields (CRF) are popular methods for labeling unstructured or textual data. Like many machine learning approaches, these undirected graphical models assume the instances to be independently distributed. However, in real-world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. We apply rule learning collectively on the predictions of an initial CRF for one context to acquire descriptions of its specific properties. Then, we utilize these descriptions as dynamic and high quality features in an additional (stacked) CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.},
  author = {Kluegl, Peter and Toepfer, Martin and Lemmerich, Florian and Hotho, Andreas and Puppe, Frank},
  interhash = {9ef3f543e4cc9e2b0ef078595f92013b},
  intrahash = {fbaab25e96dd20d96ece9d7fefdc3b4f},
  journal = {Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics},
  pages = {111-125},
  title = {Exploiting Structural Consistencies with Stacked Conditional Random Fields},
  volume = 30,
  year = 2013
}

@inproceedings{ring2015condist,
  author = {Ring, Markus and Otto, Florian and Becker, Martin and Niebler, Thomas and Landes, Dieter and Hotho, Andreas},
  editor = {ECMLPKDD2015},
  interhash = {c062a57a17a0910d6c27ecd664502ac1},
  intrahash = {a2f9d649f2856677e4d886a3b517404d},
  title = {ConDist: A Context-Driven Categorical Distance Measure},
  year = 2015
}

@inproceedings{DBLP:conf/dsaa/KrompassNT14,
  author = {Krompass, Denis and Nickel, Maximilian and Tresp, Volker},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  booktitle = {International Conference on Data Science and Advanced Analytics, {DSAA}               2014, Shanghai, China, October 30 - November 1, 2014},
  crossref = {DBLP:conf/dsaa/2014},
  doi = {10.1109/DSAA.2014.7058046},
  interhash = {0ca986606c22ca0b3780c9b9c25f31c7},
  intrahash = {c952ed96ece470e4fa5336eedf670d5b},
  isbn = {978-1-4799-6991-3},
  pages = {18--24},
  publisher = {{IEEE}},
  title = {Large-scale factorization of type-constrained multi-relational data},
  url = {http://dx.doi.org/10.1109/DSAA.2014.7058046},
  year = 2014
}

@inproceedings{noauthororeditor,
  author = {Mirowski, Piotr and Ranzato, Marc'Aurelio and LeCun, Yann},
  editor = {of the NIPS 2010 Workshop on Deep Learning, Proceedings},
  interhash = {b7ce347e904a4ca3263cf6cc1e2253bd},
  intrahash = {fc3e0e3af595f9a46df6bc9233df836f},
  title = {Dynamic Auto-Encoders for Semantic Indexing},
  url = {http://yann.lecun.com/exdb/publis/pdf/mirowski-nipsdl-10.pdf},
  year = 2010
}

@misc{yu2013largescale,
  abstract = {The multi-label classification problem has generated significant interest in
recent years. However, existing approaches do not adequately address two key
challenges: (a) the ability to tackle problems with a large number (say
millions) of labels, and (b) the ability to handle data with missing labels. In
this paper, we directly address both these problems by studying the multi-label
problem in a generic empirical risk minimization (ERM) framework. Our
framework, despite being simple, is surprisingly able to encompass several
recent label-compression based methods which can be derived as special cases of
our method. To optimize the ERM problem, we develop techniques that exploit the
structure of specific loss functions - such as the squared loss function - to
offer efficient algorithms. We further show that our learning framework admits
formal excess risk bounds even in the presence of missing labels. Our risk
bounds are tight and demonstrate better generalization performance for low-rank
promoting trace-norm regularization when compared to (rank insensitive)
Frobenius norm regularization. Finally, we present extensive empirical results
on a variety of benchmark datasets and show that our methods perform
significantly better than existing label compression based methods and can
scale up to very large datasets such as the Wikipedia dataset.},
  author = {Yu, Hsiang-Fu and Jain, Prateek and Kar, Purushottam and Dhillon, Inderjit S.},
  interhash = {1252173520757338468a68e028494647},
  intrahash = {716e5270c1dcb3a1e4eedf9934859021},
  note = {cite arxiv:1307.5101},
  title = {Large-scale Multi-label Learning with Missing Labels},
  url = {http://arxiv.org/abs/1307.5101},
  year = 2013
}

@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
}

@inproceedings{mitchell2015,
  author = {Mitchell, T. and Cohen, W. and Hruscha, E. and Talukdar, P. and Betteridge, J. and Carlson, A. and Dalvi, B. and Gardner, M. and Kisiel, B. and Krishnamurthy, J. and Lao, N. and Mazaitis, K. and Mohammad, T. and Nakashole, N. and Platanios, E. and Ritter, A. and Samadi, M. and Settles, B. and Wang, R. and Wijaya, D. and Gupta, A. and Chen, X. and Saparov, A. and Greaves, M. and Welling, J.},
  booktitle = {AAAI},
  interhash = {52d0d71f6f5b332dabc1412f18e3a93d},
  intrahash = {63070703e6bb812852cca56574aed093},
  note = {: Never-Ending Learning in AAAI-2015},
  title = {Never-Ending Learning},
  url = {http://www.cs.cmu.edu/~wcohen/pubs.html},
  year = 2015
}

@inproceedings{Carlson10,
  author = {Carlson, A. and Betteridge, J. and Kisiel, B. and Settles, B. and Jr., E.R. Hruschka and Mitchell, T.M.},
  booktitle = {Proceedings of the Conference on Artificial Intelligence (AAAI)},
  interhash = {5df31649862b1002848792cd495d46dc},
  intrahash = {f0d94ab9d299609ee92f6ecf555266d4},
  pages = {1306--1313},
  publisher = {AAAI Press},
  title = {Toward an Architecture for Never-Ending Language Learning},
  year = 2010
}

@article{cimiano05learning,
  author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen},
  ee = {http://www.jair.org/papers/paper1648.html},
  interhash = {4c09568cff62babd362aab03095f4589},
  intrahash = {eaaf0e4b3a8b29fab23b6c15ce2d308d},
  journal = {Journal on Artificial Intelligence Research},
  pages = {305-339},
  title = {Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis},
  url = {http://dblp.uni-trier.de/db/journals/jair/jair24.html#CimianoHS05},
  volume = 24,
  year = 2005
}

@inproceedings{conf/pkdd/BalasubramanyanDC13,
  author = {Balasubramanyan, Ramnath and Dalvi, Bhavana Bharat and Cohen, William W.},
  booktitle = {ECML/PKDD (2)},
  crossref = {conf/pkdd/2013-2},
  editor = {Blockeel, Hendrik and Kersting, Kristian and Nijssen, Siegfried and Zelezný, Filip},
  ee = {http://dx.doi.org/10.1007/978-3-642-40991-2_40},
  interhash = {9a32b7cc059a500ea302d0aa65036682},
  intrahash = {e56623d21a1b7bcb442cd15fe098bb70},
  isbn = {978-3-642-40990-5},
  pages = {628-642},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering.},
  url = {http://dblp.uni-trier.de/db/conf/pkdd/pkdd2013-2.html#BalasubramanyanDC13},
  volume = 8189,
  year = 2013
}

@inproceedings{joachims99,
  address = {Cambridge, MA, USA},
  author = {Joachims, Thorsten},
  booktitle = {Advances in Kernel Methods - Support Vector Learning},
  editor = {Sch\"olkopf, Bernhard and Burges, Christopher J.C. and Smola, A.},
  interhash = {f97179c7ebe10f64411417f9e05563a8},
  intrahash = {dc79351cca889847d9d20c7ef9dafa25},
  publisher = {MIT Press},
  title = {{Making Large-Scale SVM Learning Practical}},
  year = 1999
}

@inproceedings{bullock2011tagging,
  author = {Bullock, Beate Navarro and Jäschke, Robert and Hotho, Andreas},
  booktitle = {Proceedings of the ACM WebSci'11},
  interhash = {7afaa67dfeb07f7e0b85abf2be61aff1},
  intrahash = {493e03868a98f498628cad31f9320e9f},
  month = {June},
  title = {Tagging data as implicit feedback for learning-to-rank},
  url = {http://journal.webscience.org/463/},
  year = 2011
}

@article{Hazman:30May2009:1744-2621:24,
  abstract = {Ontologies play a vital role in many web- and internet-related applications. This work presents a system for accelerating the ontology building process via semi-automatically learning a hierarchal ontology given a set of domain-specific web documents and a set of seed concepts. The methods are tested with web documents in the domain of agriculture. The ontology is constructed through the use of two complementary approaches. The presented system has been used to build an ontology in the agricultural domain using a set of Arabic extension documents and evaluated against a modified version of the AGROVOC ontology.},
  author = {Hazman, Maryam and El-Beltagy, Samhaa R. and Rafea, Ahmed},
  doi = {doi:10.1504/IJMSO.2009.026251},
  interhash = {fe27d687bcba91a7a6fe51eec9a2b87d},
  intrahash = {323c8bdedc8a4643232a498ac03d6407},
  journal = {International Journal of Metadata, Semantics and Ontologies},
  pages = {24-33(10)},
  title = {Ontology learning from domain specific web documents},
  url = {http://www.ingentaconnect.com/content/ind/ijmso/2009/00000004/F0020001/art00003},
  volume = 4,
  year = 2009
}

@inproceedings{conf/ecai/HjelmB08,
  author = {Hjelm, Hans and Buitelaar, Paul},
  booktitle = {ECAI},
  crossref = {conf/ecai/2008},
  editor = {Ghallab, Malik and Spyropoulos, Constantine D. and Fakotakis, Nikos and Avouris, Nikolaos M.},
  ee = {http://dx.doi.org/10.3233/978-1-58603-891-5-288},
  interhash = {21a658154fb1a02e773b7a678b15f9f4},
  intrahash = {813903a333a40ecf9a59ded552acb323},
  isbn = {978-1-58603-891-5},
  pages = {288-292},
  publisher = {IOS Press},
  series = {Frontiers in Artificial Intelligence and Applications},
  title = {Multilingual Evidence Improves Clustering-based Taxonomy Extraction.},
  url = {http://www.ling.su.se/staff/hans/artiklar/ecai2008-hjelm-buitelaar.pdf},
  volume = 178,
  year = 2008
}

@incollection{cimiano2009ontology,
  abstract = {Ontology learning techniques serve the purpose of supporting an ontology
engineer in the task of creating and maintaining an ontology. In this chapter, we present a comprehensive and concise introduction to the field of ontology learning.
We present a generic architecture for ontology learning systems and discuss its main components. In addition, we introduce the main problems and challenges addressed in the field and give an overview of the most important methods applied. We conclude with a brief discussion of advanced issues which pose interesting challenges to the state-of-the-art.},
  author = {Cimiano, P. and Mädche, A. and Staab, S. and Völker, J.},
  booktitle = {Handbook on Ontologies},
  edition = {2nd revised edition},
  editor = {Staab, S. and Studer, R.},
  interhash = {5387f28040285a086ab706bc33e7d7af},
  intrahash = {f9f8bb0af1a8a514c270f83237313ac7},
  pages = {245--267},
  publisher = {Springer},
  series = {International Handbooks on Information Systems},
  title = {Ontology Learning},
  url = {http://www.uni-koblenz.de/~staab/Research/Publications/2009/handbookEdition2/ontology-learning-handbook2.pdf},
  year = 2009
}

@article{journals/kais/GuoV08,
  author = {Guo, Hongyu and Viktor, Herna L.},
  date = {2010-02-02},
  ee = {http://dx.doi.org/10.1007/s10115-008-0127-5},
  interhash = {430e66171506726f2478939162b789b6},
  intrahash = {35bee198e0b131ead007dd91d794ab78},
  journal = {Knowl. Inf. Syst.},
  number = 3,
  pages = {287-312},
  title = {Multirelational classification: a multiple view approach.},
  url = {http://dblp.uni-trier.de/db/journals/kais/kais17.html#GuoV08},
  volume = 17,
  year = 2008
}

@inproceedings{conf/cikm/ZhangTLW07,
  author = {Zhang, Duo and Tang, Jie and Li, Juan-Zi and Wang, Kehong},
  booktitle = {CIKM},
  crossref = {conf/cikm/2007},
  date = {2007-12-10},
  editor = {Silva, Mário J. and Laender, Alberto H. F. and Baeza-Yates, Ricardo A. and McGuinness, Deborah L. and Olstad, Bjørn and Olsen, Øystein Haug and Falcão, André O.},
  ee = {http://doi.acm.org/10.1145/1321440.1321600},
  interhash = {c46bb587aa7f5ca30e77e257e2f3c5b0},
  intrahash = {210a9f932dc4c454e8a84ca6eb4906d8},
  isbn = {978-1-59593-803-9},
  pages = {1019-1022},
  publisher = {ACM},
  title = {A constraint-based probabilistic framework for name disambiguation.},
  url = {http://dblp.uni-trier.de/db/conf/cikm/cikm2007.html#ZhangTLW07},
  year = 2007
}

@inproceedings{plangprasopchok2009,
  abstract = {Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use user-specified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection/set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies.},
  address = {New York, NY, USA},
  author = {Plangprasopchok, A. and Lerman, K.},
  booktitle = {WWW '09: Proceedings of the 18th international conference on World wide web},
  doi = {http://doi.acm.org/10.1145/1526709.1526814},
  interhash = {fccd894a82edb040d7438d6da91e3ebe},
  intrahash = {559ee9d48f1a510f56765b2357aa8ea5},
  isbn = {978-1-60558-487-4},
  location = {Madrid, Spain},
  pages = {781--790},
  publisher = {ACM},
  title = {Constructing folksonomies from user-specified relations on flickr},
  url = {http://www2009.org/proceedings/pdf/p781.pdf},
  year = 2009
}

@incollection{Voelker2008,
  author = {V\"{o}lker, J. and Haase, P. and Hitzler, P.},
  booktitle = {Ontology Learning and Population: Bridging the Gap between Text and 	Knowledge},
  interhash = {cf638e52ae5c4f9af9d35d8faee72c16},
  intrahash = {f80af45b8659db1a4327a5ce1df3f267},
  owner = {blev},
  publisher = {IOS Press},
  series = {Frontiers in Artificial Intelligence and Applications},
  timestamp = {2009.02.22},
  title = {Learning Expressive Ontologies},
  year = 2008
}