research-article

Using linked data to mine RDF from wikipedia's tables

Authors:

Alessandra MileoAuthors Info & Claims

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

Pages 533 - 542

https://doi.org/10.1145/2556195.2556266

Published: 24 February 2014 Publication History

Abstract

The tables embedded in Wikipedia articles contain rich, semi-structured encyclopaedic content. However, the cumulative content of these tables cannot be queried against. We thus propose methods to recover the semantics of Wikipedia tables and, in particular, to extract facts from them in the form of RDF triples. Our core method uses an existing Linked Data knowledge-base to find pre-existing relations between entities in Wikipedia tables, suggesting the same relations as holding for other entities in analogous columns on different rows. We find that such an approach extracts RDF triples from Wikipedia's tables at a raw precision of 40%. To improve the raw precision, we define a set of features for extracted triples that are tracked during the extraction phase. Using a manually labelled gold standard, we then test a variety of machine learning methods for classifying correct/incorrect triples. One such method extracts 7.9 million unique and novel RDF triples from over one million Wikipedia tables at an estimated precision of 81.5%.

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Cited By

Zhong LWu JLi QPeng HWu X(2023)A Comprehensive Survey on Automatic Knowledge Graph ConstructionACM Computing Surveys10.1145/361829556:4(1-62)Online publication date: 5-Sep-2023
https://dl.acm.org/doi/10.1145/3618295
Suhara YLi JLi YZhang DDemiralp ÇChen CTan WIves ZBonifati AEl Abbadi A(2022)Annotating Columns with Pre-trained Language ModelsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517906(1493-1503)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517906
Trabelsi MChen ZZhang SDavison BHeflin J(2022)StruBERT: Structure-aware BERT for Table Search and MatchingProceedings of the ACM Web Conference 202210.1145/3485447.3511972(442-451)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3511972
Show More Cited By

Index Terms

Using linked data to mine RDF from wikipedia's tables
1. Information systems
  1. Information systems applications
    1. Data mining
  2. World Wide Web
    1. Web applications
    2. Web services

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cover image ACM Conferences

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

February 2014

712 pages

ISBN:9781450323512

DOI:10.1145/2556195

General Chairs:
Ben Carterette
University of Delaware, USA
,
Fernando Diaz
Microsoft Research, USA
,
Program Chairs:
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Donald Metzler
Google, USA

Copyright © 2014 ACM.

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Published: 24 February 2014

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WSDM 2014: Seventh ACM International Conference on Web Search and Data Mining

February 24 - 28, 2014

New York, New York, USA

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WSDM '14 Paper Acceptance Rate 64 of 355 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Zhong LWu JLi QPeng HWu X(2023)A Comprehensive Survey on Automatic Knowledge Graph ConstructionACM Computing Surveys10.1145/361829556:4(1-62)Online publication date: 5-Sep-2023
https://dl.acm.org/doi/10.1145/3618295
Suhara YLi JLi YZhang DDemiralp ÇChen CTan WIves ZBonifati AEl Abbadi A(2022)Annotating Columns with Pre-trained Language ModelsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517906(1493-1503)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517906
Trabelsi MChen ZZhang SDavison BHeflin J(2022)StruBERT: Structure-aware BERT for Table Search and MatchingProceedings of the ACM Web Conference 202210.1145/3485447.3511972(442-451)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3511972
Tong SShen DKou YNie T(2022)CFCT: The cell function classification method for complex tables2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00326(2206-2213)Online publication date: Dec-2022
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00326
Chen SKaraoglu ANegreanu CMa TYao JWilliams JJiang FGordon ALin C(2022)LinkingPark: An automatic semantic table interpretation systemJournal of Web Semantics10.1016/j.websem.2022.10073374(100733)Online publication date: Oct-2022
https://doi.org/10.1016/j.websem.2022.100733
Shigarov AMikhailov A(2022)Rule-based spreadsheet data transformation from arbitrary to relational tablesInformation Systems10.1016/j.is.2017.08.00471:C(123-136)Online publication date: 13-Apr-2022
https://dl.acm.org/doi/10.1016/j.is.2017.08.004
Shigarov A(2022)Table understanding: Problem overviewWIREs Data Mining and Knowledge Discovery10.1002/widm.148213:1Online publication date: 21-Nov-2022
https://doi.org/10.1002/widm.1482
Bouchiha D(2021)Reengineering Legacy Systems Towards New TechnologiesEncyclopedia of Information Science and Technology, Fifth Edition10.4018/978-1-7998-3479-3.ch084(1214-1230)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-3479-3.ch084
Wang DShiralkar PLockard CHuang BDong XJiang M(2021)TCN: Table Convolutional Network for Web Table InterpretationProceedings of the Web Conference 202110.1145/3442381.3450090(4020-4032)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3450090
Zhang SBalog K(2021)Semantic Table Retrieval Using Keyword and Table QueriesACM Transactions on the Web10.1145/344169015:3(1-33)Online publication date: 13-May-2021
https://dl.acm.org/doi/10.1145/3441690
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