Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management |
| Publikationsstatus | Veröffentlicht - 2015 |
| Veranstaltung | The 24th ACM International Conference on Information and Knowledge Management - Melbourne, Australien Dauer: 19 Okt. 2015 → 23 Okt. 2015 Konferenznummer: 24 |
Abstract
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CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 2015.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung
}
TY - GEN
T1 - Automated News Suggestions for Populating Wikipedia Entity Pages
AU - Fetahu, Besnik
AU - Markert, Katja
AU - Anand, Avishek
N1 - Conference code: 24
PY - 2015
Y1 - 2015
N2 - 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 \emph{salience} and \emph{relative authority} of entities, and the \emph{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 \emph{article-entity} suggestion stage and upto 84\% for the \emph{article-section placement}. Finally, we compare our approach against competitive baselines and show significant improvements.
AB - 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 \emph{salience} and \emph{relative authority} of entities, and the \emph{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 \emph{article-entity} suggestion stage and upto 84\% for the \emph{article-section placement}. Finally, we compare our approach against competitive baselines and show significant improvements.
KW - cs.IR
KW - cs.CL
KW - cs.SI
U2 - 10.1145/2806416.2806531
DO - 10.1145/2806416.2806531
M3 - Conference contribution
SN - 978-1-4503-3794-6
BT - CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
T2 - The 24th ACM International Conference on Information and Knowledge Management
Y2 - 19 October 2015 through 23 October 2015
ER -