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Web-scale knowledge extraction from semi-structured tables

Authors:

Eric Crestan,

Patrick PantelAuthors Info & Claims

WWW '10: Proceedings of the 19th international conference on World wide web

Pages 1081 - 1082

https://doi.org/10.1145/1772690.1772814

Published: 26 April 2010 Publication History

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Abstract

A wealth of knowledge is encoded in the form of tables on the World Wide Web. We propose a classification algorithm and a rich feature set for automatically recognizing layout tables and attribute/value tables. We report the frequencies of these table types over a large analysis of the Web and propose open challenges for extracting from attribute/value tables semantic triples (knowledge). We then describe a solution to a key problem in extracting semantic triples: protagonist detection, i.e., finding the subject of the table that often is not present in the table itself. In 79% of our Web tables, our method finds the correct protagonist in its top three returned candidates.

References

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Cited By

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Gangadhar GKulkarni A(2022)Extraction of Product Specifications from the Web - Going Beyond Tables and ListsProceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)10.1145/3493700.3493713(19-27)Online publication date: 8-Jan-2022
https://dl.acm.org/doi/10.1145/3493700.3493713
Khairova NPetrasova SMamyrbayev OMukhsina K(2020)Open Information Extraction as Additional Source for Kazakh Ontology GenerationIntelligent Information and Database Systems10.1007/978-3-030-41964-6_8(86-96)Online publication date: 4-Mar-2020
https://doi.org/10.1007/978-3-030-41964-6_8
Keshvari-Fini PJanfada BMinaei-Bidgoli B(2019)A Survey on Knowledge Extraction Techniques for Web Tables2019 5th International Conference on Web Research (ICWR)10.1109/ICWR.2019.8765271(123-127)Online publication date: Apr-2019
https://doi.org/10.1109/ICWR.2019.8765271
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Index Terms

Web-scale knowledge extraction from semi-structured tables
1. Computing methodologies
  1. Machine learning
    1. Learning settings

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Information

Published In

WWW '10: Proceedings of the 19th international conference on World wide web

April 2010

1407 pages

ISBN:9781605587998

DOI:10.1145/1772690

General Chairs:
Michael Rappa
North Carolina State University, USA
,
Paul Jones
University of North Carolina at Chapel Hill, USA
,
Program Chairs:
Juliana Freire
University of Utah, USA
,
Soumen Chakrabarti
Indian Institute of Technology, India

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2010

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WWW '10

WWW '10: The 19th International World Wide Web Conference

April 26 - 30, 2010

North Carolina, Raleigh, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Gangadhar GKulkarni A(2022)Extraction of Product Specifications from the Web - Going Beyond Tables and ListsProceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)10.1145/3493700.3493713(19-27)Online publication date: 8-Jan-2022
https://dl.acm.org/doi/10.1145/3493700.3493713
Khairova NPetrasova SMamyrbayev OMukhsina K(2020)Open Information Extraction as Additional Source for Kazakh Ontology GenerationIntelligent Information and Database Systems10.1007/978-3-030-41964-6_8(86-96)Online publication date: 4-Mar-2020
https://doi.org/10.1007/978-3-030-41964-6_8
Keshvari-Fini PJanfada BMinaei-Bidgoli B(2019)A Survey on Knowledge Extraction Techniques for Web Tables2019 5th International Conference on Web Research (ICWR)10.1109/ICWR.2019.8765271(123-127)Online publication date: Apr-2019
https://doi.org/10.1109/ICWR.2019.8765271
Milosevic NGregson CHernandez RNenadic G(2019)A framework for information extraction from tables in biomedical literatureInternational Journal on Document Analysis and Recognition10.1007/s10032-019-00317-022:1(55-78)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s10032-019-00317-0
Khairova NPetrasova SGautam A(2016)The Logical-Linguistic Model of Fact Extraction from English TextsInformation and Software Technologies10.1007/978-3-319-46254-7_51(625-635)Online publication date: 22-Sep-2016
https://doi.org/10.1007/978-3-319-46254-7_51
Liu QWu DLiu YCheng X(2014)Extracting Attributes and Synonymous Attributes from Online EncyclopediasProceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 0110.1109/WI-IAT.2014.46(290-296)Online publication date: 11-Aug-2014
https://dl.acm.org/doi/10.1109/WI-IAT.2014.46
Cruz IGanesh VMirrezaei S(2013)Semantic extraction of geographic data from web tables for big data integrationProceedings of the 7th Workshop on Geographic Information Retrieval10.1145/2533888.2533939(19-26)Online publication date: 5-Nov-2013
https://dl.acm.org/doi/10.1145/2533888.2533939
Ying Liu Gong-Qing Wu Xue-Gang Hu (2013)A web table extraction algorithm based on tree edit distanceIEEE Conference Anthology10.1109/ANTHOLOGY.2013.6784738(1-6)Online publication date: Jan-2013
https://doi.org/10.1109/ANTHOLOGY.2013.6784738
Sainani AReddy PMaheshwari S(2012)Mining special features to improve the performance of e-commerce product selection and resume processingInternational Journal of Computational Science and Engineering10.1504/IJCSE.2012.0461837:1(82-95)Online publication date: 1-Mar-2012
https://dl.acm.org/doi/10.1504/IJCSE.2012.046183
Rinfret DChênevert CDrolet F(2011)OSD-DBProceedings of the 13th Asia-Pacific web conference on Web technologies and applications10.5555/1996794.1996856(440-449)Online publication date: 18-Apr-2011
https://dl.acm.org/doi/10.5555/1996794.1996856
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Towards domain-independent information extraction from web tables

Web-scale table census and classification

Towards web-scale structured web data extraction

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