research-article

Cross-lingual knowledge linking across wiki knowledge bases

Authors:

Jie TangAuthors Info & Claims

WWW '12: Proceedings of the 21st international conference on World Wide Web

Pages 459 - 468

https://doi.org/10.1145/2187836.2187899

Published: 16 April 2012 Publication History

Abstract

Wikipedia becomes one of the largest knowledge bases on the Web. It has attracted 513 million page views per day in January 2012. However, one critical issue for Wikipedia is that articles in different language are very unbalanced. For example, the number of articles on Wikipedia in English has reached 3.8 million, while the number of Chinese articles is still less than half million and there are only 217 thousand cross-lingual links between articles of the two languages. On the other hand, there are more than 3.9 million Chinese Wiki articles on Baidu Baike and Hudong.com, two popular encyclopedias in Chinese. One important question is how to link the knowledge entries distributed in different knowledge bases. This will immensely enrich the information in the online knowledge bases and benefit many applications. In this paper, we study the problem of cross-lingual knowledge linking and present a linkage factor graph model. Features are defined according to some interesting observations. Experiments on the Wikipedia data set show that our approach can achieve a high precision of 85.8% with a recall of 88.1%. The approach found 202,141 new cross-lingual links between English Wikipedia and Baidu Baike.

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Cited By

Zhang FLiu XTang JDong YYao PZhang JGu XWang YKharlamov EShao BLi RWang K(2023)OAG: Linking Entities Across Large-Scale Heterogeneous Knowledge GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322216835:9(9225-9239)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TKDE.2022.3222168
Bipat TAlimohammadi NYu YMcDonald DZachry M(2021)Wikipedia Beyond the English Language EditionProceedings of the ACM on Human-Computer Interaction10.1145/34491295:CSCW1(1-39)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3449129
Liu XMian LDong YZhang FZhang JTang JZhang PGong JWang K(2021)OAG_know: Self-supervised Learning for Linking Knowledge GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3090830(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3090830
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Index Terms

Cross-lingual knowledge linking across wiki knowledge bases
1. Information systems
  1. Information storage systems
    1. Record storage systems
      1. Record storage alternatives
        Linked lists

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cover image ACM Other conferences

WWW '12: Proceedings of the 21st international conference on World Wide Web

April 2012

1078 pages

ISBN:9781450312295

DOI:10.1145/2187836

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Copyright © 2012 ACM.

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Univ. de Lyon: Universite de Lyon

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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WWW 2012

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Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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Zhang FLiu XTang JDong YYao PZhang JGu XWang YKharlamov EShao BLi RWang K(2023)OAG: Linking Entities Across Large-Scale Heterogeneous Knowledge GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322216835:9(9225-9239)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TKDE.2022.3222168
Bipat TAlimohammadi NYu YMcDonald DZachry M(2021)Wikipedia Beyond the English Language EditionProceedings of the ACM on Human-Computer Interaction10.1145/34491295:CSCW1(1-39)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3449129
Liu XMian LDong YZhang FZhang JTang JZhang PGong JWang K(2021)OAG_know: Self-supervised Learning for Linking Knowledge GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3090830(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3090830
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