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Tabular data analysis is performed every day across various domains.
The dimensional fact model: A conceptual model for data warehouses
Matteo Golfarelli, Dario Maio, and Stefano Rizzi. 1998 · 1998
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The dimensional fact model: A conceptual model for data warehouses
Matteo Golfarelli, Dario Maio, and Stefano Rizzi. 1998 · 1998
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Automatic discovery of attributes in relational databases
Meihui Zhang, Marios Hadjieleftheriou, Beng Chin Ooi, Cecilia M Procopiuc, and Divesh Srivastava. 2011 · 2011
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Instance-based ‘one-to-some’assignment of similarity measures to attributes
Tobias Vogel and Felix Naumann. 2011 · 2011
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Meihui Zhang, Marios Hadjieleftheriou, Beng Chin Ooi, Cecilia M Procopiuc, and Divesh Srivastava. 2011 · 2011
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Jingjing Wang, Haixun Wang, Zhongyuan Wang, and Kenny Q. Zhu. 2012 · 2012
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Understanding tables on the web
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The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling
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The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling
R. Kimball and M. Ross. 2013 · 2013
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Sunita Sarawagi and Soumen Chakrabarti. 2014 · 2014
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Wikidata: a free collaborative knowledgebase
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Open-domain quantity queries on web tables: annotation, response, and consensus models
Sunita Sarawagi and Soumen Chakrabarti. 2014 · 2014
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Wikidata: a free collaborative knowledgebase
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Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. 2015 · 2015
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Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. 2015 · 2015
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Language to logical form with neural attention
Li Dong and Mirella Lapata. 2016 · 2016
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A large public corpus of web tables containing time and context metadata
Oliver Lehmberg, Dominique Ritze, Robert Meusel, and Christian Bizer. 2016 · 2016
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Language to logical form with neural attention
Li Dong and Mirella Lapata. 2016 · 2016
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A large public corpus of web tables containing time and context metadata
Oliver Lehmberg, Dominique Ritze, Robert Meusel, and Christian Bizer. 2016 · 2016
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Matching web tables to dbpedia - a feature utility study
Dominique Ritze and Christian Bizer. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Entitables: Smart assistance for entity-focused tables
Shuo Zhang and Krisztian Balog. 2017 · 2017
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Matching web tables to dbpedia - a feature utility study
Dominique Ritze and Christian Bizer. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Entitables: Smart assistance for entity-focused tables
Shuo Zhang and Krisztian Balog. 2017 · 2017
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Data profiling
Ziawasch Abedjan, Lukasz Golab, Felix Naumann, and Thorsten Papenbrock. 2018 · 2018
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Using tableau to visualize data and drive decision-making
Jamie Hoelscher and Amanda Mortimer. 2018 · 2018
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Making sense of numerical data - semantic labelling of web tables
Emilia Kacprzak, José M. Giménez-García, Alessandro Piscopo, Laura Koesten, Luis-Daniel Ibáñez, Jeni Tennison, and Elena Simperl. 2018 · 2018
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Recognizing quantity names for tabular data
Yang Yi, Zhiyu Chen, Jeff Heflin, and Brian D Davison. 2018 · 2018
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Data profiling
Ziawasch Abedjan, Lukasz Golab, Felix Naumann, and Thorsten Papenbrock. 2018 · 2018
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Using tableau to visualize data and drive decision-making
Jamie Hoelscher and Amanda Mortimer. 2018 · 2018
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Making sense of numerical data - semantic labelling of web tables
Emilia Kacprzak, José M. Giménez-García, Alessandro Piscopo, Laura Koesten, Luis-Daniel Ibáñez, Jeni Tennison, and Elena Simperl. 2018 · 2018
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Recognizing quantity names for tabular data
Yang Yi, Zhiyu Chen, Jeff Heflin, and Brian D Davison. 2018 · 2018
Natural key discovery in wikipedia tables
Leon Bornemann, Tobias Bleifuß, Dmitri V Kalashnikov, Felix Naumann, and Divesh Srivastava. 2020 · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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Tough tables: Carefully evaluating entity linking for tabular data
Vincenzo Cutrona, Federico Bianchi, Ernesto Jiménez-Ruiz, and Matteo Palmonari. 2020 · 2020
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Turl: Table understanding through representation learning
Xiang Deng, Huan Sun, Alyssa Lees, You Wu, and Cong Yu. 2020 · 2020
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TaPas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Eisenschlos. 2020 · 2020
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Si brochure: The international system of units (si)
Mesures BIdPe. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Quickinsights: Quick and automatic discovery of insights from multi-dimensional data
Rui Ding, Shi Han, Yong Xu, Haidong Zhang, and Dongmei Zhang. 2019 · 2019
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Sherlock: A deep learning approach to semantic data type detection
Madelon Hulsebos, Kevin Hu, Michiel Bakker, Emanuel Zgraggen, Arvind Satyanarayan, Tim Kraska, Çagatay Demiralp, and César Hidalgo. 2019 · 2019
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Semtab 2019: Resources to benchmark tabular data to knowledge graph matching systems
Ernesto Jiménez-Ruiz, Oktie Hassanzadeh, Vasilis Efthymiou, Jiaoyan Chen, and Kavitha Srinivas. 2020 · 2019
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Mtab: Matching tabular data to knowledge graph using probability models
P. Nguyen, N. Kertkeidkachorn, R. Ichise, and Hideaki Takeda. 2019 · 2019
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What are data insights to professional visualization users?
Po-Ming Law, Alex Endert, and John Stasko. 2020 · 2020
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Web table extraction, retrieval, and augmentation: A survey
Shuo Zhang and Krisztian Balog. 2020 · 2020
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Table2analysis: Modeling and recommendation of common analysis patterns for multi-dimensional data
Mengyu Zhou, Wang Tao, Ji Pengxin, Han Shi, and Zhang Dongmei. 2020 · 2020
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Iso 80000-1:2009 quantities and units
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TABBIE: Pretrained representations of tabular data
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A Deep Dive into Deep Learning Approaches for Text-to-SQL Systems , page 2846–2851. Association for Computing Machinery
George Katsogiannis-Meimarakis and Georgia Koutrika. 2021 · 2021
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Tcn: Table convolutional network for web table interpretation
Daheng Wang, Prashant Shiralkar, Colin Lockard, Binxuan Huang, Xin Luna Dong, and Meng Jiang. 2021 · 2021
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Ai4vis: Survey on artificial intelligence approaches for data visualization
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Grappa: Grammar-augmented pre-training for table semantic parsing
Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, Bailin Wang, Yi Chern Tan, Xinyi Yang, Dragomir Radev, Richard Socher, and Caiming Xiong. 2021 · 2021
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Table2charts: Recommending charts by learning shared table representations
Mengyu Zhou, Qingtao Li, Xinyi He, Yuejiang Li, Yibo Liu, Wei Ji, Shi Han, Yining Chen, Daxin Jiang, and Dongmei Zhang. 2021 · 2021
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Iso 80000-1:2009 quantities and units
2021
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TABBIE: Pretrained representations of tabular data
Hiroshi Iida, Dung Thai, Varun Manjunatha, and Mohit Iyyer. 2021 · 2021
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A Deep Dive into Deep Learning Approaches for Text-to-SQL Systems , page 2846–2851. Association for Computing Machinery
George Katsogiannis-Meimarakis and Georgia Koutrika. 2021 · 2021
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Tcn: Table convolutional network for web table interpretation
Daheng Wang, Prashant Shiralkar, Colin Lockard, Binxuan Huang, Xin Luna Dong, and Meng Jiang. 2021 · 2021
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Ai4vis: Survey on artificial intelligence approaches for data visualization
Aoyu Wu, Yun Wang, Xinhuan Shu, Dominik Moritz, Weiwei Cui, Haidong Zhang, Dongmei Zhang, and Huamin Qu. 2021 · 2021
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Grappa: Grammar-augmented pre-training for table semantic parsing
Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, Bailin Wang, Yi Chern Tan, Xinyi Yang, Dragomir Radev, Richard Socher, and Caiming Xiong. 2021 · 2021
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Table2charts: Recommending charts by learning shared table representations
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Table pre-training: A survey on model architectures, pre-training objectives, and downstream tasks
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