Automated News Suggestions for Populating Wikipedia Entity Pages
Automated News Suggestions for Populating Wikipedia Entity Pages
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DOI：
10.1145/2806416.2806531
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发表时间：
2015-10
期刊：
影响因子：
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通讯作者：
B. Fetahu;K. Markert;Avishek Anand
中科院分区：
文献类型：
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作者：
B. Fetahu;K. Markert;Avishek Anand
维基百科实体页面是直接消费和知识库建设、更新和维护的宝贵信息来源。这些实体页面中的事实通常由引用支持。最近的研究表明，多达20%的参考文献来自在线新闻来源。然而，许多实体页面是不完整的，即使相关信息已经在现有的新闻文章中可用。即使对于已经存在的参考文献，在新闻文章发布时间和参考文献时间之间也经常存在延迟。因此，在这项工作中，我们通过新闻的透镜来看待维基百科，并提出了一种新颖的新闻文章建议任务，以改善维基百科的新闻报道，并减少有新闻价值的参考文献的滞后。我们的工作直接应用于维基百科页面生成和知识库加速任务，这些任务依赖于相关和高质量的输入源。我们提出了一个两阶段的监督方法，建议新闻文章的实体页面为一个给定的状态的维基百科。首先，我们建议新闻文章的维基百科实体（文章实体放置）依赖于一组丰富的功能，考虑到实体的显着性和相对权威性，以及新闻文章的新奇，以实体页面。其次，我们确定输入文章（文章部分放置）的实体页面中的确切部分，由基于类的部分模板指导。我们根据从维基百科的外部参考文献中提取的地面实况数据对我们的方法进行了广泛的评估。我们在文章实体建议阶段实现了高达93%的高精度值，在文章部分放置时高达84%。最后，我们将我们的方法与竞争基线进行比较，并显示出显着的改进。
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.