Automated News Suggestions for Populating Wikipedia Entity Pages

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Automated News Suggestions for Populating Wikipedia Entity Pages - scientific work related to Wikipedia quality published in 2015, written by Besnik Fetahu, Katja Markert and Avishek Anand.

Overview

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, authors 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. Authors 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. Authors propose a two-stage supervised approach for suggesting news articles to entity pages for a given state of Wikipedia. First, authors 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, authors determine the exact section in the entity page for the input article (article-section placement) guided by class-based section templates. Authors perform an extensive evaluation of approach based on ground-truth data that is extracted from external references in Wikipedia. Authors achieve a high precision value of up to 93% in the article-entity suggestion stage and upto 84% for the article-section placement . Finally, authors compare approach against competitive baselines and show significant improvements.