research-article

Automated News Suggestions for Populating Wikipedia Entity Pages

Authors:

Avishek AnandAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 323 - 332

https://doi.org/10.1145/2806416.2806531

Published: 17 October 2015 Publication History

Abstract

Wikipedia entity pages are a valuable source of information for direct consumption and for knowledge-base construction, update and maintenance. Facts in these entity pages are typically supported by references. Recent studies show that as much as 20% of the references are from online news sources. However, many entity pages are incomplete even if relevant information is already available in existing news articles. Even for the already present references, there is often a delay between the news article publication time and the reference time. In this work, we therefore look at Wikipedia through the lens of news and propose a novel news-article suggestion task to improve news coverage in Wikipedia, and reduce the lag of newsworthy references. Our work finds direct application, as a precursor, to Wikipedia page generation and knowledge-base acceleration tasks that rely on relevant and high quality input sources.

We propose a two-stage supervised approach for suggesting news articles to entity pages for a given state of Wikipedia. First, we suggest news articles to Wikipedia entities (article-entity placement) relying on a rich set of features which take into account the salience and relative authority of entities, and the novelty of news articles to entity pages. Second, we determine the exact section in the entity page for the input article (article-section placement) guided by class-based section templates. We perform an extensive evaluation of our approach based on ground-truth data that is extracted from external references in Wikipedia. We achieve a high precision value of up to 93% in the article-entity suggestion stage and upto 84% for the article-section placement. Finally, we compare our approach against competitive baselines and show significant improvements.

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Cited By

Zhang JWang JWang XLi ZXiao YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware EntitiesProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614782(3361-3370)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614782
Lyu LAnand A(2023)Listwise Explanations for Ranking Models Using Multiple ExplainersAdvances in Information Retrieval10.1007/978-3-031-28244-7_41(653-668)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-28244-7_41
Chatterjee SDietz LAl Hasan MXiong L(2022)Predicting Guiding Entities for Entity Aspect LinkingProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557671(3848-3852)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557671
Show More Cited By

Index Terms

Automated News Suggestions for Populating Wikipedia Entity Pages
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 October 2015

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ERC Advanced Grant ALEXANDRIA

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CIKM'15

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

Melbourne, Australia

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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

Zhang JWang JWang XLi ZXiao YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware EntitiesProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614782(3361-3370)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614782
Lyu LAnand A(2023)Listwise Explanations for Ranking Models Using Multiple ExplainersAdvances in Information Retrieval10.1007/978-3-031-28244-7_41(653-668)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-28244-7_41
Chatterjee SDietz LAl Hasan MXiong L(2022)Predicting Guiding Entities for Entity Aspect LinkingProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557671(3848-3852)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557671
Chatterjee SDietz LAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)BERT-ERProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531944(1466-1477)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531944
Chatterjee S(2022)An Entity-Oriented Approach for Answering Topical Information NeedsAdvances in Information Retrieval10.1007/978-3-030-99739-7_57(463-472)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-030-99739-7_57
Ramsdell JDietz Ld'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)A Large Test Collection for Entity Aspect LinkingProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412875(3109-3116)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412875
Bai XCambazoglu BGullo FMantrach ASilvestri F(2020)Improving News Personalization Through Search LogsBias and Social Aspects in Search and Recommendation10.1007/978-3-030-52485-2_14(152-166)Online publication date: 12-Jul-2020
https://doi.org/10.1007/978-3-030-52485-2_14
Lu WLiu JXu WWang PWei B(2019)EncyCatalogRec: catalog recommendation for encyclopedia article completionFrontiers of Information Technology & Electronic Engineering10.1631/FITEE.180036321:3(436-447)Online publication date: 5-Sep-2019
https://doi.org/10.1631/FITEE.1800363
Redi MFetahu BMorgan JTaraborelli D(2019)Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia's VerifiabilityThe World Wide Web Conference10.1145/3308558.3313618(1567-1578)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313618
Ponza MFerragina PPiccinno F(2019) Swat : A system for detecting salient Wikipedia entities in texts Computational Intelligence10.1111/coin.1221635:4(858-890)Online publication date: 6-May-2019
https://doi.org/10.1111/coin.12216
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