Never-Ending Language Learning (NELL)


Never-Ending Language Learning {related:}

See also:

Active Learning & Dialog Systems | Agent Building and Learning Environment (ABLE) | Artificial Intelligence In Learning Management Systems (LMS) | Best BigML VideosBest Machine Learning Videos | CLASSiC (Computational Learning in Adaptive Systems for Spoken Conversation) | Deep Learning & Dialog Systems | github: deep learning | Learning Classifier & Dialog Systems | LMS (Learning Management System) & Dialog Systems | Machine Learning | Machine Learning & Chatbots | Machine Learning as a Service (MLaaS) | PLOW (Procedure Learning on the Web) | Selectional Preference Learning


Populating the semantic web by macro-reading internet text TM Mitchell, J Betteridge, A Carlson… – The Semantic Web- …, 2009 – Springer … techniques with the newly promoted instances. Fig- ure 1 shows some facts extracted by a recent run of the system (see the complete results at http://rtw.ml.cmu.edu/ readtheweb.html). In a recent experiment involving 16 categories … Cited by 40 Related articles All 13 versions Cite Save

Entity list completion using set expansion techniques B Dalvi, J Callan, W Cohen – 2011 – DTIC Document … Coupled semi-supervised learning for information extraction. In WSDM, 2010. [5] T. Mitchell. Read the web project. http://rtw.ml.cmu.edu/rtw/. [6] RC Wang and WW Cohen. Language- independent set expansion of named entities using the web. In ICDM, 2007. … Cited by 12 Related articles All 14 versions Cite Save

Read the web T Mitchell, W Cohen, JE Hruschka, B Settles, D Wijaya… – 2010 – hickory.eol.org … KB freely available on the web • Java and Matlab API’s available • file:///Users/tommitchell/ Documents/trunk/data/ tom/bkisiel_aaai10_08m. 075.domainrange.xmldir/index.html • http://rtw.ml.cmu.edu/wk/kb/bkisiel_aaai10_08m. 075.domainrange.xmldir Page 6. Why Do This? … Cited by 3 Related articles All 3 versions Cite Save More

Collective intelligence as a source for machine learning self-supervision SDS Pedro, ER Hruschka Jr – … of the 4th International Workshop on Web …, 2012 – dl.acm.org … As stated in NELL’s website (http://rtw.ml.cmu.edu), in a nutshell, the system has as inputs (1) an initial ontology defining hundreds of categories and relations that NELL is expected to read about, (2) 10 to 15 seed examples of each category and relation and (3) a col- lection of … Cited by 4 Related articles All 5 versions Cite Save

Bootstrapping the linked data web D Gerber, ACN Ngomo – 1st Workshop on Web Scale Knowledge …, 2011 – files.ifi.uzh.ch … to generate RDF from text. 2 http://www.alchemyapi.com 3 http://www.opencalais. org 4 http://extractiv.com 5 http://fox.aksw.org 6 http://rtw.ml.cmu.edu 7 http://lemurproject.org/clueweb09 Page 4. 3 Approach In this section we … Cited by 21 Related articles All 3 versions Cite Save More

Multi-step classification approaches to cumulative citation recommendation K Balog, H Ramampiaro, N Takhirov… – Proceedings of the 10th …, 2013 – dl.acm.org … Meij et al. [29] propose an approach to analyse queries submitted to search engines, automatically identify concepts that are related to the queries, and then link the queries 2http://rtw.ml.cmu.edu/ rtw/. 3http://ai.cs.washington.edu/projects/ open-information-extraction. … Cited by 8 Related articles All 5 versions Cite Save

Coupled semi-supervised learning for information extraction A Carlson, J Betteridge, RC Wang… – Proceedings of the third …, 2010 – dl.acm.org Page 1. Coupled Semi-Supervised Learning for Information Extraction Andrew Carlson Schoool of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 acarlson@cs.cmu.edu Justin Betteridge Schoool of Computer … Cited by 170 Related articles All 16 versions Cite Save

Empirical studies in learning to read M Freedman, E Loper, E Boschee… – Proceedings of the …, 2010 – dl.acm.org … In the study in this paper, recall, precision, and F are measured for 11 relations under the following contrastive conditions 1 http://www.nist.gov/speech/tests/ace/ 2 http://apl.jhu.edu/~paulmac/kbp. html 3 See http://rtw.ml.cmu.edu/papers/mitchell-iswc09.pdf 61 Page 2. … Cited by 3 Related articles All 10 versions Cite Save

Which noun phrases denote which concepts? J Krishnamurthy, TM Mitchell – Proceedings of the 49th Annual Meeting …, 2011 – dl.acm.org … Categories are single-argument pred- icates, and relations are two-argument predicates. 1More information about NELL, including browsable and downloadable versions of its knowledge base, is available from http://rtw.ml.cmu.edu. 572 Page 4. … Cited by 7 Related articles All 17 versions Cite Save

Collectively representing semi-structured data from the web B Dalvi, WW Cohen, J Callan – Proceedings of the Joint Workshop on …, 2012 – dl.acm.org … Carlson, A., Betteridge, J., Wang, RC, Hruschka, Jr., ER, and Mitchell, TM (2010). Coupled semi- supervised learning for information extraction. In WSDM. http://rtw.ml.cmu.edu/rtw/. Dalvi, B., Callan, J., and Cohen, W. (2010). Entity list completion using set expansion techniques. … Cited by 4 Related articles All 16 versions Cite Save

Understanding semantic change of words over centuries DT Wijaya, R Yeniterzi – Proceedings of the 2011 international workshop …, 2011 – dl.acm.org … Section 5. We conclude with future works in Section 6. 1http://www.ruf.rice.edu/? kemmer/Words04/meaning 2NELL: http://rtw.ml.cmu.edu/rtw 35 Page 2. 2. RELATED WORKS A quantitative study on cultural trends: ‘culturomics … Cited by 7 Related articles All 3 versions Cite Save

Extracting multilingual natural-language patterns for rdf predicates D Gerber, ACN Ngomo – Knowledge Engineering and Knowledge …, 2012 – Springer … Throughout this description, we will use the example of generating new knowledge for the dbpedia:architect relation. 2 http://www.alchemyapi.com 3 http://rtw.ml.cmu.edu 4 http://lemurproject.org/clueweb09 5 http://dumps.wikimedia.org/ Page 4. 90 D. Gerber and A.- … Cited by 10 Related articles All 8 versions Cite Save

Websets: Extracting sets of entities from the web using unsupervised information extraction BB Dalvi, WW Cohen, J Callan – … of the fifth ACM international conference …, 2012 – dl.acm.org … entities along with labeling each of them. The datasets and evaluations done for these experiments are posted online at http://rtw.ml.cmu.edu/ wk/WebSets/ wsdm_2012_online/index.html. 4.1 Datasets To test the performance … Cited by 28 Related articles All 16 versions Cite Save

A generative entity-mention model for linking entities with knowledge base X Han, L Sun – Proceedings of the 49th Annual Meeting of the …, 2011 – dl.acm.org … each other. One of the most notorious examples is Wikipedia: its 2010 English 1 http://www.wikipedia.org/ 2 http://rtw.ml.cmu.edu/ version contains more than 3 million entities and 20 million semantic relations. Bridging these … Cited by 40 Related articles All 8 versions Cite Save

Integrating Open and Closed Information Extraction: Challenges and First Steps A Dutta, C Meilicke, M Niepert… – Proceedings of 1st … – publications.wim.uni-mannheim.de … 4 http://rtw.ml.cmu.edu/rtw/resources Page 5. Linking Information Extraction Projects 5 Top predicates Instances Random predicates Instances generalizations 1297709 personleads- organization 716 proxyfor 5540 countrylocatedingeopoliticallocation 632 … Cited by 2 Related articles All 3 versions Cite Save More

Exploiting Ontology Structures and Unlabeled Data for Learning N Balcan, A Blum, Y Mansour – … of The 30th International Conference on …, 2013 – jmlr.org … Motivation A prime example motivating our work is the NELL Never-Ending-Language-Learning sys- tem (Carlson et al., 2010a;b) (rtw.ml.cmu.edu) which has had substantial success in learning a wide-range classification of objects in the world from primarily unlabeled data. … Cited by 1 Related articles All 9 versions Cite Save

Autonomously reviewing and validating the knowledge base of a never-ending learning system SDS Pedro, AP Appel, ER Hruschka Jr – Proceedings of the 22nd …, 2013 – dl.acm.org … for future human supervision. Currently, NELL’s developers (see http://rtw.ml.cmu. edu/people) are responsible for human supervision in NELL’s KB and they limit this verification task in 5 minutes a day. Time restriction is imposed … Cited by 1 Related articles All 4 versions Cite Save

Introduction to Artificial Intelligence CSJ Urbain – jayurbain.com … of millions of web pages (eg, playsInstrument(George_Harrison, guitar)). ? Second, it attempts to improve its reading competence, so that tomorrow it can extract more facts from the web, more accurately. ? http://rtw.ml.cmu.edu/rtw/ Page 35. Current topics … Cited by 1 Related articles All 2 versions Cite Save More

Toward an Architecture for Never-Ending Language Learning. A Carlson, J Betteridge, B Kisiel, B Settles… – AAAI, 2010 – aaai.org … precision, (4) all patterns promoted by CPL, and (5) all rules learned by RL. 3http://rtw.ml.cmu.edu/aaai10_online Predicate Feature Weight mountain LAST=peak 1.791 mountain LAST=mountain 1.093 mountain FIRST=mountain … Cited by 336 Related articles All 22 versions Cite Save

Discovering relations between noun categories TP Mohamed, ER Hruschka Jr, TM Mitchell – Proceedings of the …, 2011 – dl.acm.org … ex- tracted knowledge base. Note also that the outputs * Thahir P. Mohamed is currently at Amazon Inc. 1 NELL?s extracted knowledge can be viewed and downloaded at http://rtw.ml.cmu.edu. 1447 Page 2. of our system are … Cited by 36 Related articles All 14 versions Cite Save

Scoup-SMT: Scalable Coupled Sparse Matrix-Tensor Factorization EE Papalexakis, TM Mitchell, ND Sidiropoulos… – arXiv preprint arXiv: …, 2013 – arxiv.org Page 1. arXiv:1302.7043v1 [stat.ML] 28 Feb 2013 Scoup-SMT: Scalable Coupled Sparse Matrix-Tensor Factorization Evangelos E. Papalexakis Carnegie Mellon University epapalex@cs.cmu.edu Tom M. Mitchell Carnegie Mellon University tom.mitchell@cmu.edu … Cited by 1 Related articles All 4 versions Cite Save

Nell2RDF: Read the Web, and turn it into RDF A Zimmermann, C Gravier, J Subercaze… – Proceedings of the …, 2013 – ceur-ws.org … During its constant reading of Web pages (now several billions), the confidence of certain facts reaches a threshold that is sufficient to consider the fact to be true. Such facts are published online on the Web site http://rtw.ml.cmu.edu/rtw/ and a Twitter bot publishes those facts1. … Related articles All 5 versions Cite Save More

Keynote Talks TM Mitchell – … Learning and Applications (ICMLA), 2010 Ninth …, 2010 – ieeexplore.ieee.org … As of April, it had extracted a structured knowledge base containing approximately a third of a million beliefs. You can track its progress at http://rtw.ml.cmu.edu/readtheweb.html. Published in: Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on. … All 2 versions Cite Save

Automatic Text Understanding of Content and Text Quality ANI NENKOVA – chinatranslation.net Page 1. Copyright © National Academy of Sciences. All rights reserved. Frontiers of Engineering 2011: Reports on Leading-Edge Engineering from the 2011 Symposium 49 Automatic Text Understanding of Content and Text Quality ANI NENKOVA University of Pennsylvania … Related articles All 6 versions Cite Save More

Probabilistic models for uncertain data P Senellart – Proceedings of the Fourth Symposium on …, 2013 – pierre.senellart.com … Page 5. Use case: Web information extraction Never-ending Language Learning (NELL, CMU), http://rtw.ml.cmu.edu/rtw/kbbrowser/ Page 6. Use case: Web information extraction Google Squared (terminated), screenshot from [Fink et al., 2011] Page 7. … All 3 versions Cite Save More

Contradiction Detection and Ontology Extension in a Never-Ending Learning System V Oliverio, ER Hruschka Jr – Advances in Artificial Intelligence–IBERAMIA …, 2012 – Springer … The three facts above, are not, by themselves, contradictory. But, when putting them all together, they represent a contradiction. In other words, if Joe Smith 1 http://rtw.ml.cmu.edu Page 3. Contradiction Detection and Ontology Extension in a NELL 3 … Related articles All 3 versions Cite Save

Entity Correspondence with Second-Order Markov Logic Y Xu, Z Gao, C Wilson, Z Zhang, M Zhu, Q Ji – Web Information Systems …, 2013 – Springer … In addi- tion, we prove that second-order Markov Logic can be rephrased to first-order in practice, which bridges these two models theoretically. 1 http://rtw.ml.cmu.edu/rtw/ 2 http://www.w3.org/ 2001/sw/ Page 3. Entity Correspondence with Second-Order Markov Logic 3 … Related articles Cite Save

Course Project E Xing, A Singh – Machine Learning, 2011 – cs.cmu.edu … Two versions of the SVO data are here (verb non-stemmed and stemmed): http://rtw.ml.cmu.edu/ppt/v+prep_svo-triples_stemmed.txt.gz (220,015,169 triples) http://rtw.ml.cmu.edu/ppt/v+prep_svo-triples.txt.gz (220,462,606 triples). … Related articles All 2 versions Cite Save More

Linking book characters toward a corpus encoding relations between entities D Cristea, E Ignat – … Dialogue (SpeD), 2013 7th Conference on, 2013 – ieeexplore.ieee.org … examples. Over its exploitation, the extracted knowledge had to be manually revised several times as to eliminate erroneous facts, 1 http://rtw.ml.cmu.edu/rtw/ which, let to proliferate, would have deteriorated its future behaviour. … Related articles Cite Save

Towards entity search: Research roadmap M Laclavík, M Ciglan – laclavik.sk … With Annotowatch we have participate in MSM 2013 Information Extraction challenge15, where we have finished at 2nd place, missing first 12 http://rtw.ml.cmu.edu/rtw/ 13 https://github.com/ knowitall 14 http://ikt.ui.sav.sk/ 15 http://oak.dcs.shef.ac.uk/msm2013/challenge.html 163 … Related articles Cite Save More

An ontology-driven reading agent DW Lonsdale, DW Embley, SW Liddle – deg.byu.edu … access methods. 1See http://research.microsoft.com/en-us/projects/mindnet/default. aspx. 2See http://rtw.ml.cmu.edu/rtw/. 3For more details see http://www.darpa.mil/ Our Work/I2O/Programs/Machine Reading.aspx. 4See http … Related articles Cite Save More

Demonstration of the FDB query engine for factorised databases N Bakibayev, D Olteanu, J Závodný – Proceedings of the VLDB …, 2012 – dl.acm.org … IsIn stadium city CampNou Barcelona Wembley London Stamford London Figure 1: A sample from the NELL database. A constantly evolving database is available at rtw.ml.cmu.edu. T1: T2: T3: T4: team player league stadium league team player stadium city team stadium team … Related articles All 7 versions Cite Save

Final Report A SVM Model for Relation Classification of Noun Phrases based on the NELL Database SC Xu, X Yan, W Zhang – cs.cmu.edu … In Proceedings of the 43rd Annual Meeting on Association for Computional Linguistics, pp 427-434. [10] NELL: Never-Ending Language Learning. http://rtw.ml.cmu.edu/rtw/ [11] The ClueWeb09 Dataset. http://www.lemurproject.org/clueweb09.php/ 8 Related articles Cite Save More

Classifying entities into an incomplete ontology B Dalvi, WW Cohen, J Callan – Proceedings of the 2013 workshop on …, 2013 – dl.acm.org … Bayesian models for large-scale hierarchical classification. [10] T. Mitchell. Nell: Never-ending language learning. http://rtw.ml.cmu.edu/rtw/. [11] TP Mohamed, ER Hruschka Jr, and TM Mitchell. Discovering relations between noun categories. In EMNLP, 2011. [12] NIST. … Related articles All 5 versions Cite Save

Defining (Human) Computation E Law – ACM CHI Conference on Human Factors in Computing …, 2011 – crowdresearch.org Page 1. Defining (Human) Computation Edith Law Carnegie Mellon University Pittsburgh, PA, 15217 edith@cmu.edu ABSTRACT Human computation is a term that has been used synonymously with other related concepts, including … Cited by 1 Related articles Cite Save More

Improving Learning and Inference in a Large Knowledge-base using Latent Syntactic Cues M Gardner, PP Talukdar, B Kisiel, T Mitchell – oldsite.aclweb.org … that corresponds to the verb. Similarly, for the most negative k 2 columns. 1This data and other resources from the paper are publicly available at http://rtw.ml.cmu.edu/ emnlp2013 pra/. 835 Page 4. Precision Recall F1 PRA 0.800 … Related articles All 8 versions Cite Save More

Parcube: Sparse parallelizable tensor decompositions EE Papalexakis, C Faloutsos… – Machine Learning and …, 2012 – Springer Page 1. ParCube: Sparse Parallelizable Tensor Decompositions Evangelos E. Papalexakis1,? , Christos Faloutsos1, and Nicholas D. Sidiropoulos2,?? 1 School of Computer Science, Carnegie Mellon University, Pittsburgh … Cited by 13 Related articles All 6 versions Cite Save

Coupling semi-supervised learning of categories and relations A Carlson, J Betteridge, ER Hruschka Jr… – Proceedings of the …, 2009 – dl.acm.org … 3See http://rtw.ml.cmu.edu/sslnlp09 for re- sults from a full run of the system. Est. CBL Freebase Est. New Category Prec. Instances Matches Instances Actor 100 522 465 57 Athlete 100 117 54 63 Board Game 89 18 6 10 City 100 1799 1665 134 Company 100 1937 995 942 … Cited by 59 Related articles All 20 versions Cite Save

Affective Common Sense Knowledge Acquisition for Sentiment Analysis. E Cambria, Y Xia, A Hussain – LREC, 2012 – lrec.elra.info … The fill-in-the blank questions, in turn, are sen- tences to be completed such as “opening a Christmas gift makes feel ”. 4http://freebase.com 5http://mpi-inf.mpg.de/yago-naga/yago 6http://rtw.ml.cmu.edu/rtw 7http://research.microsoft.com/probase 8http://omcsentics.labs.sitekit. … Cited by 4 Related articles All 5 versions Cite Save More

Random walk inference and learning in a large scale knowledge base N Lao, T Mitchell, WW Cohen – … of the Conference on Empirical Methods …, 2011 – dl.acm.org … Although NELL has now grown a sizable knowl- edge base, its ability to perform inference over this 1NELL’s current KB is available online at http://rtw.ml.cmu.edu. Eli Manning Giants AthletePlays ForTeam HinesWard Steelers AthletePlays ForTeam NFL TeamPlays InLeague … Cited by 36 Related articles All 18 versions Cite Save

Community-based classification of noun phrases in twitter FCT Chua, WW Cohen, J Betteridge… – Proceedings of the 21st …, 2012 – dl.acm.org … tweet as an individual document, this approach was previously applied in TwitterRank [13]. 1 http://rtw.ml.cmu.edu 2 http://lemurproject.org/clueweb09.php/ 1704 Page 4. Formally, given a set of tweets Ta written by an author a … Cited by 3 Related articles All 12 versions Cite Save

Predicting Tasks in Goal-Oriented Spoken Dialog Systems using Semantic Knowledge Bases A Pappu, AI Rudnicky – aclweb.org … We build our models on the man- ual transcriptions from the training data and eval- uate on the ASR hypotheses of the testing data. 2http://www.princeton.edu/wordnet/download/ 3https://www.googleapis.com/freebase/v1/search 4http://rtw.ml.cmu.edu/rtw/resources … Related articles All 7 versions Cite Save More

Conversing Learning: Actively looking for human assistance to improve Machine Learning tasks SDS Pedro, ER Hruschka Jr – openreview.net … Toward an architecture for never-ending language learning. In Proceedings of AAAI2010, volume 2, pages 1306–1313, 2010. 1http://rtw.ml.cmu.edu [2] D. Cohn, Z. Ghahramani, and M. Jordan. Active learning with statistical models. … Related articles All 2 versions Cite Save More

Fine-Grained Entity Recognition. X Ling, DS Weld – AAAI, 2012 – aaai.org … extractor, ?pred. A true positive is 9We excluded the relations having inadequate ground tuples for training. 10http://rtw.ml.cmu.edu/rtw/resources granted if and only if r(e1,e2) exists in both ?test and ?pred. The precision / recall … Cited by 17 Related articles All 4 versions Cite Save

Multi-Task Active Learning with Output Constraints. Y Zhang – AAAI, 2010 – aaai.org … We use 6 classes of 708 named entities (noun phrases) extracted by the system, which include 242 animals, 107 mammals, 200 companies3, 2http://rtw.ml.cmu.edu/readtheweb. html 3We use a subset of companies to not overwhelm other classes. … Cited by 22 Related articles All 7 versions Cite Save

Learning Verbs on the Fly. Z Kozareva – COLING (Posters), 2012 – oldsite.aclweb.org … produced by our learning procedure. 2http://web.media.mit.edu/˜hugo/conceptnet/# overview 3Comparison done in March 2012 with http://rtw.ml.cmu.edu/rtw/kbbrowser/ 604 Page 7. Term Accuracy Verbs Accuracy Arguments … Related articles All 7 versions Cite Save More

The Hidden Web, XML, and the Seman $ c Web: A Scien $ fic Data Management Perspec $ ve FM Suchanek, A Varde, R Nayak, P Senellart – suchanek.name Page 1. The Hidden Web, XML, and the Seman$c Web: A Scien$fic Data Management Perspec$ve Fabian M. Suchanek, Aparna Varde, Richi Nayak, Pierre Senellart 3h Tutorial at EDBT 2011 Page 2. Overview • Introduc\on • The Hidden Web • XML • DSML • The Seman\c Web … Related articles All 2 versions Cite Save More

Turbo-SMT: Accelerating Coupled Sparse Matrix-Tensor Factorizations by 200x EE Papalexakis, TM Mitchell, ND Sidiropoulos… – cs.cmu.edu Page 1. Turbo-SMT: Accelerating Coupled Sparse Matrix-Tensor Factorizations by 200x Evangelos E. Papalexakis? epapalex@cs.cmu.edu Tom M. Mitchell ? tom.mitchell@cmu.edu Nicholas D. Sidiropoulos † nikos@ece.umn.edu … Related articles All 2 versions Cite Save More

GraphDB–Storing Large Graphs on Secondary Memory LF Navarro, AP Appel, ERH Junior – New Trends in Databases and …, 2014 – Springer … However, in many settings, we want to express asymmetric relationships, for example, A points to B but not vice versa. For this purpose, we define a directed graph as a set of nodes (same as in the indirected case) together 1 http://rtw.ml.cmu.edu/rtw/ Page 3. … Related articles All 2 versions Cite Save

Never-Ending Knowledge Base Expansion Through Continuous Machine Reading PH Barchi, ER Hruschka Jr – openreview.net … gener- ate new relations combined with NEL techniques. The pre- processing stage prepares the input corpus to be processed 8NELL/Read The Web page: http://rtw.ml.cmu.edu. Algorithm: Relation Generation Input: One pair of … Related articles Cite Save More

WebSets: Unsupervised Information Extraction approach to Extract Sets of Entities from the Web B Dalvi, W Cohen, J Callan – 2011 – cs.cmu.edu Page 1. WebSets : Unsupervised Information Extraction approach to Extract Sets of Entities from the Web [Extended Abstract] ? Bhavana Dalvi School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 bbd@cs.cmu.edu … Related articles All 4 versions Cite Save More

PATTY: a taxonomy of relational patterns with semantic types N Nakashole, G Weikum, F Suchanek – … of the 2012 Joint Conference on …, 2012 – dl.acm.org Page 1. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 1135–1145, Jeju Island, Korea, 12–14 July 2012. cO2012 Association for Computational Linguistics … Cited by 44 Related articles All 11 versions Cite Save

Perpetual learning through overcoming inconsistencies D Zhang – Proc. of the 25th IEEE International Conference on …, 2013 – ecs.csus.edu Page 1. Perpetual Learning through Overcoming Inconsistencies Du Zhang Department of Computer Science California State University Sacramento, CA 95819-6021, USA zhangd@ecs.csus.edu Abstract— This paper provides … Cited by 1 Related articles Cite Save More

Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic. S Jiang, D Lowd, D Dou – ICDM, 2012 – ix.cs.uoregon.edu … Machine Learning, vol. 3, no. 1, pp. 1–155, 2009. [5] T. Mitchell, W. Cohen, J. Estevam Hruschka, B. Settles, D. Wijaya, E. Law, J. Betteridge, J. Krishnamurthy, and B. Kisiel, “Read the web,” http://rtw.ml.cmu.edu/rtw/. [6] A. Carlson … Cited by 6 Related articles All 5 versions Cite Save More

DinTucker: Scaling up Gaussian process models on multidimensional arrays with billions of elements S Zhe, Y Qi, Y Park, I Molloy, S Chari – arXiv preprint arXiv:1311.2663, 2013 – arxiv.org Page 1. arXiv:1311.2663v1 [cs.LG] 12 Nov 2013 DinTucker: Scaling up Gaussian process models on multidimensional arrays with billions of elements Shandian Zhe Purdue University szhe@purdue.edu Yuan Qi Purdue University alanqi@purdue.edu … Cited by 2 Related articles All 4 versions Cite Save

Ontological Smoothing for Relation Extraction with Minimal Supervision. C Zhang, R Hoffmann, DS Weld – AAAI, 2012 – aaai.org … instances. 3There is no obvious way to handle ? operators, without joint- inference or learning thresholds. 4http : //rtw.ml.cmu.edu/aaai10online/relations.xls 5LDC2010E07,theMachineReadingP1ICTrainingDataV3.1 160 Page 5. … Cited by 5 Related articles All 8 versions Cite Save

Multilabel Image Classification Via High-Order Label Correlation Driven Active Learning B. Zhang is with National ICT Australia.(e-mail: fbang. zhang@ nicta. com. au). B Zhang, Y Wang, F Chen – ieeexplore.ieee.org Page 1. 1057-7149 (c) 2013 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This … Related articles Cite Save

Conversing learning: Active learning and active social interaction for human supervision in never-ending learning systems SDS Pedro, ER Hruschka Jr – Advances in Artificial Intelligence– …, 2012 – Springer … and guidance. In this work, we intend to show how a Never-Ending Learning system (like NELL) can 1 http://rtw.ml.cmu.edu Page 3. Conversing Learning: Active Learning and Active Social Interaction 233 autonomouslyuse … Cited by 3 Related articles All 5 versions Cite Save

Introducing a New Scalable Data-as-a-Service Cloud Platform for Enriching Traditional Text Mining Techniques by Integrating Ontology Modelling and Natural Language Processing A Cheptsov, A Tenschert, P Schmidt, B Glimm… – uni-ulm.de … Page 14. 15. Carnegie Mellon University: Read the Web. http://rtw.ml.cmu.edu/rtw/, 2012. 16. U. Deriu, J. Lehmann, and P. Schmidt. Creation of a technique ontology based on filtered language technologies (in German). In Proceedings Knowtech, Bad Homburg, 2009. 17. … Related articles Cite Save More

Large Scale Tensor Decompositions: Algorithmic Developments and Applications E Papalexakis, U Kang, C Faloutsos, N Sidiropoulos… – context, 2013 – sites.computer.org … References [1] Read the web. http://rtw.ml.cmu.edu/rtw/. [2] E. Acar, C. Aykut-Bingol, H. Bingol, R. Bro, and B. Yener. Multiway analysis of epilepsy tensors. Bioinformatics, 23(13):i10–i18, 2007. [3] CA Andersson and R. Bro. The n-way toolbox for matlab. … Related articles All 4 versions Cite Save

Learning to Re?ne an Automatically Extracted Knowledge Base using Markov Logic Dept. of Comput. & Inf. Sci., Univ. of Oregon, Eugene, OR, USA S Jiang, D Lowd, D Dou – Data Mining (ICDM), 2012 IEEE 12th …, 2012 – ieeexplore.ieee.org … Machine Learning, vol. 3, no. 1, pp. 1–155, 2009. [5] T. Mitchell, W. Cohen, J. Estevam Hruschka, B. Settles, D. Wijaya, E. Law, J. Betteridge, J. Krishnamurthy, and B. Kisiel, “Read the web,” http://rtw.ml.cmu.edu/rtw/. [6] A. Carlson … Related articles Cite Save

Jointly learning to parse and perceive: Connecting natural language to the physical world J Krishnamurthy, T Kollar – Transactions of the Association for …, 2013 – rtw.ml.cmu.edu … These sections describe the data sets, features, construc- tion of the CCG lexicon, and details of the models. Our data sets and additional evaluation resources are available online from http://rtw.ml.cmu.edu/ tacl2013_lsp/. 5.1 Data Sets … Cited by 5 Related articles All 8 versions Cite Save More

Fast cache for your text: Accelerating exact pattern matching with feed-forward bloom filters I Moraru, DG Andersen – 2009 – repository.cmu.edu Page 1. Carnegie Mellon University Research Showcase Computer Science Department School of Computer Science 9-1-2009 Fast Cache for Your Text: Accelerating Exact Pattern Matching with Feed-Forward Bloom Filters Iulian Moraru Carnegie Mellon University … Cited by 3 Related articles All 14 versions Cite Save

Interview with Rudi Studer on “Semantic Technologies” U Frank – Business & Information Systems Engineering, 2010 – Springer Page 1. BISE – INTERVIEW Interview with Rudi Studer on “Semantic Technologies” Rudi Studer is professor at the Institute of Applied Informatics and Formal Description Methods (AIFB) of the Karlsruhe Institute of Technology … All 10 versions Cite Save

PIDGIN: ontology alignment using web text as interlingua D Wijaya, PP Talukdar, T Mitchell – Proceedings of the 22nd ACM …, 2013 – dl.acm.org … Keywords Ontology Alignment, Knowledge Bases, Graph-based Self- Supervised Learning, Label Propagation, Natural Language Processing. 1PIDGIN: the source code and relevant datasets will be available at http://rtw.ml.cmu.edu/cikm2013 pidgin/ … Related articles All 6 versions Cite Save

Six Step SAFARI from the Dublin Core to the Seman+ c Web R Daniel Jr – Citeseer Page 1. 10/23/10 Six Step SAFARI from the Dublin Core to the Seman+c Web Ron Daniel, Jr. Elsevier Labs Dublin Core 2010 Conference, Pittsburgh, Oct. 20, 2010 Page 2. 10/23/10 Pop Quiz: • What ques+on do you hope to get answered by being here today? 2 Page 3. … Cite Save More

Scalable knowledge harvesting with high precision and high recall N Nakashole, M Theobald, G Weikum – … on Web search and data mining, 2011 – dl.acm.org Page 1. Scalable Knowledge Harvesting with High Precision and High Recall Ndapandula Nakashole, Martin Theobald, Gerhard Weikum Max Planck Institute for Informatics Saarbrücken, Germany {nnakasho,mtb,weikum}@mpi-inf.mpg.de … Cited by 64 Related articles All 8 versions Cite Save

Using Markov Logic to Refine an Automatically Extracted Knowledge Base SJD Lowd, D Dou – ix.cs.uoregon.edu Page 1. Using Markov Logic to Refine an Automatically Extracted Knowledge Base Shangpu Jiang Daniel Lowd Dejing Dou Dept. of Computer and Information Science University of Oregon Eugene, OR 97403 {shangpu,lowd,dou}@cs.uoregon.edu Abstract … Related articles All 3 versions Cite Save More

Minimização do Impacto do Problema de Desvio de Conceito por Meio de Acoplamento em Ambiente de Aprendizado Sem Fim MC Duarte, ER Hruschka Jr, M do Carmo Nicoletti – nilc.icmc.sc.usp.br … al. 2009b]. Um exemplo de ontologia inicial pode ser visto em http://rtw.ml.cmu.edu/ readtheWeb.html, que é a própria ontologia do sistema NELL, abordado na Seção 4 deste trabalho e do qual o RTWP é parte. Os algoritmos … Related articles All 7 versions Cite Save More

Never-Ending Learning T Mitchell – 2010 – DTIC Document … Page 22. 17 8. Publications associated with this project: The publications below are available at Carnegie Mellon University’s Read the Web project website, http://rtw.ml.cmu.edu/readtheweb. html. • Coupled Semi-Supervised Learning for Information Extraction. Andrew … Related articles All 15 versions Cite Save

Centaurs-a Component Based Framework to Mine Large Graphs AP Appel, ER Hruschka Junior – Journal of Information and Data …, 2011 – seer.lcc.ufmg.br … A visualization of the giant connected component of rtwgraph is presented in Figure 24. 1http://labs.google.com/papers/mapreduce.html 2http://hadoop.apache.org/ 3http://rtw.ml.cmu.edu/rtw/ 4The authors used GraphViz to visualization … Cited by 2 Related articles All 4 versions Cite Save More

Collective entity linking in web text: a graph-based method X Han, L Sun, J Zhao – Proceedings of the 34th international ACM SIGIR …, 2011 – dl.acm.org … which is usually referred to as Entity Linking (EL for short). For example, in Figure 1 an entity linking system should link the 1 http://www.wikipedia.org 2 http://rtw.ml. cmu.edu/ Permission to make digital or hard copies of all or part … Cited by 61 Related articles All 5 versions Cite Save

Batch mode active learning for multi-label image classification with informative label correlation mining B Zhang, Y Wang, W Wang – Applications of Computer Vision ( …, 2012 – ieeexplore.ieee.org … 1/3 examples are used as testing examples. 30 examples are used to initialize prediction models. 5 ran- dom runs are performed. 3http://rtw.ml.cmu.edu/rtw/ 405 Page 6. Algorithm hamm.loss? one-error? coverage? rank.loss? ave.prec.? … Cited by 4 Related articles All 6 versions Cite Save

Coupled bayesian sets algorithm for semi-supervised learning and information extraction S Verma, ER Hruschka Jr – Machine Learning and Knowledge Discovery …, 2012 – Springer … Following NELL’s principles, we present an approach in which the input to the semi-supervised learner is an ontology defining a set of target 1 http://rtw.ml.cmu.edu Page 3. CBS for Semi supervised Learning and Information Extraction 309 … Cited by 2 Related articles All 6 versions Cite Save

Coupled semi-supervised learning A Carlson – 2010 – DTIC Document … and 0 otherwise. • ?2(f1(x1,x2),f2(x1,x2),f3(x1,x2)) is defined to be 1 if f3(x1,x2) ? f2(x1,x2) is satisfied, and 0 otherwise. 1.4 Supplementary Online Materials Numerous materials from this thesis have been put online at http://rtw.ml.cmu.edu/ acarlson_thesis. 1.4.1 Ontologies … Cited by 12 Related articles All 14 versions Cite Save

Parse reranking for domain-adaptative relation extraction F Xu, H Li, Y Zhang, H Uszkoreit… – Journal of Logic and …, 2012 – Oxford Univ Press Page 1. [12:31 6/12/2012 exs055.tex] LogCom: Journal of Logic and Computation Page: 1 1–19 Parse reranking for domain-adaptative relation extraction FEIYU XU, HONG LI, YI ZHANG, HANS USZKOREIT and SEBASTIAN … Cited by 1 Related articles All 2 versions Cite Save

Semantic rule filtering for web-scale relation extraction A Moro, H Li, S Krause, F Xu, R Navigli… – The Semantic Web– …, 2013 – Springer … Despite the gain in recall 4 NELL rules were taken from iteration 680, http://rtw.ml.cmu.edu/ resources/results/08m/ Page 10. 356 A. Moro et al. 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 2 4 6 8 10 12 14 16 bn-precision bn-recall wn-precision wn-recall 0.3 0.4 0.5 0.6 0.7 0.8 0.9 … Cited by 2 Related articles All 5 versions Cite Save

Probabilistic databases D Suciu, D Olteanu, C Ré… – Synthesis Lectures on Data …, 2011 – morganclaypool.com … Consider a simple query over the NELL database: “Retrieve all products manufactured by a company headquartered in San Jose”: select x.Product, x.Company 1 http://rtw.ml.cmu.edu/rtw/ Page 20. 2 1. OVERVIEW Figure 1.1: Facts extracted by NELL from the WWW. … Cited by 87 Related articles All 7 versions Cite Save More

Kaleidoscope: Computer-Assisted Ideation through Idea Network Exploration Z Shan, Y Zhu, T Zhao – stanford.edu Page 1. Kaleidoscope: Computer-Assisted Ideation through Idea Network Exploration Zifei Shan, Yuke Zhu and Tianxin Zhao? Department of Computer Science, Stanford University {zifei,yukez,tianxin}@stanford.edu ABSTRACT … Related articles Cite Save More

Detecting Implicit Emotion Expressions from Text Using Ontological Resources and Lexical Learning A Balahur, JM Hermida, H Tanev – New Trends of Research in Ontologies …, 2013 – Springer Logo Springer. Search Options: … Related articles All 2 versions Cite Save

Bringing Provenance to its Full Potential using Causal Reasoning A Meliou, W Gatterbauer, D Suciu – usenix.org … References [1] NELL: Never Ending Language Learning. http://rtw.ml.cmu.edu/rtw/. [2] Y. Amsterdamer, D. Deutch, and V. Tannen. Provenance for ag- gregate queries. CoRR, abs/1101.1110, 2011. [3] A. Balmin, T. Papadimitriou, and Y. Papakonstantinou. … Related articles All 7 versions Cite Save More

Never-ending learning principles in gene ontology classification using genetic algorithms L Rodrigues do Amaral… – … Computation (CEC), 2012 …, 2012 – ieeexplore.ieee.org … 1In this work we use the words ”variable” and ”attribute” as synonyms that can refer to any one of the antecedents of a classification rule, as well as, to any one of the characteristic of a term given in the Gene Ontology (GO) database 2http://rtw.ml.cmu.edu … Related articles Cite Save

Linking Book Characters E Ignat – thor.info.uaic.ro … examples. Over its exploitation, the extracted knowledge had to be manually revised several times as to eliminate erroneous facts, which, let to proliferate, would have deteriorate its future behaviour. 1 http://rtw.ml.cmu.edu/rtw/ The … Related articles All 2 versions Cite Save More

Interview mit Rudi Studer zum Thema „Semantische Technologien “ U Frank – Wirtschaftsinformatik, 2010 – Springer … 8Aktuelle Triplestores können beispielsweise sehr einfache Inferenzen über einigen Milliarden RDF Statements mit Antwortzeiten im Bereich einer Sekunde leisten. 9Vgl. http://rtw.ml.cmu.edu/ papers/mitchell-iswc09.pdf. 52 WIRTSCHAFTSINFORMATIK 1|2010 Cited by 3 Related articles All 5 versions Cite Save

Mecanismos De Busca Na Web: Passado, Presente E Futuro ICP Siqueira – PontodeAcesso, 2013 – portalseer.ufba.br … partir de uma ontologia livre e um conjunto de algoritmos para processamento semântico da linguagem natural; eo Never-Ending Language Learning (NELL), (rtw.ml.cmu.edu/rtw), idealizado para ser um agente computacional inteligente. Trata-se de um mecanismo que … Related articles Cite Save

Collecting Common Sense Associations M SVENSSON, R BRÄNNSTRÖM, P HOLMLUND – 2013 – pure.ltu.se Page 1. BACHELOR THESIS MindMatch Collecting Common Sense Associations Morgan Svensson 2013 Bachelor of Science in Engineering Technology Computer Engineering Luleå University of Technology Department … Related articles Cite Save More

Aprendizado Semissupervisionado AtravéS De TéCnicas De Acoplamento MC DUARTE – 2011 – bdtd.ufscar.br … de textos em português, assim como já haviam trazido em tarefas de extração de conhecimento a partir de textos em inglês (como pode ser visto nos resultados do projeto RTW (http://rtw.ml.cmu.edu/) disponíveis em: Page 22. 20 … Related articles All 2 versions Cite Save

Uma Linguagem para Auxiliar no Processo de Extração de Informações da Web GF Gomes, AA Bezerra – die.ufpi.br … Mitchell, T. (2009). How Will We Populate the Semantic Web on Vast Scale. http://rtw.ml.cmu. edu/slides/RTW_ISWC_mod_Oct2009.pdf, Outubro. Gorakavi, PK (2010). Gnu Prolog for Java Project. http://savannah.gnu.org/projects/gnuprologjava. Maio. … Related articles Cite Save More

PACE: Pattern Accurate Computationally Efficient Bootstrapping for Timely Discovery of Cyber-Security Concepts N McNeil, RA Bridges, MD Iannacone, B Czejdo… – arXiv preprint arXiv: …, 2013 – arxiv.org Page 1. PACE: Pattern Accurate Computationally Efficient Bootstrapping for Timely Discovery of Cyber-Security Concepts Nikki McNeil Department of Mathematics University of Maryland, Baltimore County Baltimore, MD ncmcneiL1@umbc.edu … Related articles All 4 versions Cite Save

Prophet–A Link-Predictor to Learn New Rules on NELL AP Appel, ER Hruschka – Data Mining Workshops (ICDMW), …, 2011 – ieeexplore.ieee.org … Figure 1 shows an example of two relations connected by their predicates, creating an open triangle (a triangle without one edge). Predicting a link in rtwgraph can be, in a simplistic 1http://rtw.ml.cmu.edu/rtw/ 2011 11th IEEE International Conference on Data Mining Workshops … Cited by 5 Related articles All 5 versions Cite Save

Specifying Latent Structure Characteristics in Mixed-membership Models R Balasubramanyan – 2013 – cs.cmu.edu Page 1. Specifying Latent Structure Characteristics in Mixed-membership Models Ramnath Balasubramanyan – rbalasub@cs.cmu.edu CMU-LTI-13-007 Language Technologies Institute School of Computer Science Carnegie … Related articles Cite Save More

Prophet AP Appel, ERH Junior – dc.ufscar.br … Figure 1 shows an example of two 1http://rtw.ml.cmu.edu/rtw/ Page 2. relations connected by their predicates, creating an open triangle (a triangle without one edge). Sport Team Team Plays in League Basketball NBA Milwaukee Bucks … Related articles Cite Save More

Transgenic: An evolutionary algorithm operator L Rodrigues do Amaral, R Hruschka Jr – Neurocomputing, 2014 – Elsevier Traditionally, many evolutionary algorithm operators have biological inspiration. Genetics has contributed to the proposal of a number of different evolutionary. Related articles All 2 versions Cite Save

DetecçãO De ContradiçõEs Em Um Sistema De Aprendizado Sem Fim V OLIVERIO – 2012 – bdtd.ufscar.br Page 1. UNIVERSIDADE FEDERAL DE SÃO CARLOS CENTRO DE CIÊNCIAS EXATAS E DE TECNOLOGIA DETECÇÃO DE CONTRADIÇÕES EM UM SISTEMA DE APRENDIZADO SEM FIM VINICIUS OLIVERIO ORIENTADOR: PROF. DR. … Related articles All 2 versions Cite Save

Multilabel Image Classification Via High-Order Label Correlation Driven Active Learning B Zhang, Y Wang, F Chen – IEEE Transactions on Image …, 2014 – ieeexplore.ieee.org Page 1. 1430 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 3, MARCH 2014 Multilabel Image Classification via High-Order Label Correlation Driven Active Learning Bang Zhang, Member, IEEE, Yang Wang … Cite Save

Learning Relation Networks for Relational Retrieval N Lao – 2011 – Citeseer Page 1. Learning Relation Networks for Relational Retrieval Ph.D. Thesis Proposal Mar 2, 2011 Ni Lao Language Technologies Institute School of Computer Science Carnegie Mellon University Thesis Committee William W … Related articles All 3 versions Cite Save More

Linguistic Co-Reference Analysis using Semi-Supervised Semantic Knowledge Bases SSSK Bases – 2012 – researchgate.net Page 1. Manuel Warum, Bakk. techn. 0360430 Diplomarbeit zum Thema Linguistic Co-Reference Analysis using Semi-Supervised Semantic Knowledge Bases Masterarbeit zur Erlangung des akademischen Grades Diplom-Ingenieur … Related articles Cite Save More

Exact pattern matching with feed-forward bloom filters I Moraru, DG Andersen – Journal of Experimental Algorithmics (JEA), 2012 – dl.acm.org Page 1. Exact Pattern Matching with Feed-Forward Bloom Filters IULIAN MORARU and DAVID G. ANDERSEN, Carnegie Mellon University This article presents a new, memory efficient and cache-optimized algorithm for simultaneously … Cited by 11 Related articles All 15 versions Cite Save

A context aware, mobile system providing memory support for ageing people M Migliardi, M Gaudina – MIPRO, 2011 Proceedings of the 34th …, 2011 – ieeexplore.ieee.org … URL http://rtw.ml.cmu.edu/readtheweb.html, 2010 [13] TM Mitchell, J. Betteridge, A. Carlson, E. Hruschka, and R. Wang, Populating the Semantic Web by Macro-Reading Internet Text, Invited paper, Proceedings of the 8th International Semantic Web Conference (ISWC 2009). … Cited by 1 Related articles Cite Save

Active Personal Information Manager: A System for Human Memory Support M Migliardi, M Gaudina – Complex, Intelligent and Software …, 2011 – ieeexplore.ieee.org … URL http://rtw.ml.cmu.edu/readtheweb.html, 2010 [11] Tom M. Mitchell, Justin Betteridge, Andrew Carlson, Estevam Hr- uschka, and Richard Wang, Populating the Semantic Web by Macro- Reading Internet Text, Invited paper, Proceedings of the 8th International Semantic Web … Cited by 6 Related articles All 4 versions Cite Save

[BOOK] How we think: Digital media and contemporary technogenesis NK Hayles – 2012 – books.google.com Page 1. – -lulllrl-IMII-11–IIr¢I~=-lull-ur:-I-III1:+-I11-1|Iw-I-up-I-in–m-ill-luv-I1-1- nw?hunmarwmn-b-wnhrhmnnnumunqnmhminunluau–n+nmh:-b-MI1 I’IIFl\l1l14“’ l3I’II’F-II’-PIIUIJI-I|*lI’41’-‘?Ill4L’ll-Ill’ll-HIlHq?°I”J_HIlI’II-‘4-ll-II-HIII=llI\-P … Cited by 64 Related articles All 2 versions Cite Save More

Toward a General Framework for Words and Pictures A Berg, T Berg, H Daumé III, J Dodge, A Goyal, X Han… – old-site.clsp.jhu.edu Page 1. Toward a General Framework for Words and Pictures Alexander C. Berg Stony Brook University Tamara L. Berg Stony Brook University Hal Daumé III University of Maryland Jesse Dodge University of Washington Amit … Related articles All 2 versions Cite Save More

Mapping Dependency Relationships into Semantic Frame Relationships N de Silva, C Fernando, M Maldeniya, DNC Wijeratne… – cse.mrt.ac.lk Page 1. Mapping Dependency Relationships into Semantic Frame Relationships NHND de Silva1, CSNJ Fernando1, MKDT Maldeniya1, DNC Wijeratne1, AS Perera1, B. Goertzel2 1Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka. … Related articles Cite Save More

Pour une approche centrée sur l’utilisateur en Traitement Automatique des Langues P BEUST – 2013 – beust.users.greyc.fr Page 1. Texto ! Volume XVIII – n°2 (2013) Pour une approche centrée sur l’utilisateur en Traitement Automatique des Langues Quelles instrumentations des utilisateurs dans les environnements numériques de travail ? Pierre BEUST Avril 2013 Page 2. Page 3. à Delphine, … Related articles All 2 versions Cite Save More

Pour une démarche centrée sur l’utilisateur dans les Environnements Numériques de Travail: apport au Traitement Automatique des Langues M VALETTE, E INaLCO – greyc.fr Page 1. HABILITATION À DIRIGER DES RECHERCHES de l’Université de Caen Basse-Normandie présentée et soutenue publiquement le 3 avril 2013 par Pierre BEUST Pour une démarche centrée sur l’utilisateur dans les Environnements Numériques de Travail : … Related articles All 2 versions Cite Save More

From Findability to Awareness: Metadata in Music and Technology Enhanced Learning (Vinden en beseffen: Metadata in muziek en E-learning) S Govaerts – 2012 – lirias.kuleuven.be Page 1. Arenberg Doctoral School of Science, Engineering & Technology Faculty of Engineering Department of Computer Science From Findability to Awareness: Metadata in Music and Technology Enhanced Learning. Sten GOVAERTS … Related articles Cite Save

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