WikiRef：维基链接作为为科学维基百科页面推荐适当参考的途径
WikiRef: Wikilinks as a route to recommending appropriate references for scientific Wikipedia pages
摘要：
维基百科作为研究人员科学知识的主要来源，其使用量呈指数级增长，这使得绝对有必要将这一知识库转变为一个完整和独立的信息来源，以供直接利用。不幸的是，支持每个维基百科实体页面内容的参考文献还远远不够完整。为什么大多数维基百科页面的参考部分格式不好？这一部分是否和页面的其他部分一样经常被编辑？是否有适当的替代品可以自动增强参考部分？在本文中，我们提出了一种新的两步方法- WikiRef -（i）利用维基链接存在于科学维基百科的目标页面，从而，（ii）建议高度相关的参考文献被包括在该目标页面适当和自动借用的维基链接的参考部分。在第一步中，我们构建一个分类器来确定维基链接是否是潜在的参考来源。在接下来的步骤中，我们会从维基链接的参考文献部分推荐对目标页面的参考文献，这些参考文献在第一步中被归类为潜在的参考文献来源。我们从两个不同的领域--计算机科学和物理学的数据集上对我们的方法进行了广泛的评估。对于计算机科学，我们实现了非常好的性能，参考建议的精度@1为0.44，而不是从最具竞争力的基线获得的0.38。对于Physics数据集，我们获得了类似的性能提升，相对于最具竞争力的基线提高了10%。
英文摘要：
The exponential increase in the usage of Wikipedia as a key source of scientific knowledge among the researchers is making it absolutely necessary to metamorphose this knowledge repository into an integral and self-contained source of information for direct utilization. Unfortunately, the references which support the content of each Wikipedia entity page, are far from complete. Why are the reference section ill-formed for most Wikipedia pages? Is this section edited as frequently as the other sections of a page? Can there be appropriate surrogates that can automatically enhance the reference section? In this paper, we propose a novel two step approach -- WikiRef -- that (i) leverages the wikilinks present in a scientific Wikipedia target page and, thereby, (ii) recommends highly relevant references to be included in that target page appropriately and automatically borrowed from the reference section of the wikilinks. In the first step, we build a classifier to ascertain whether a wikilink is a potential source of reference or not. In the following step, we recommend references to the target page from the reference section of the wikilinks that are classified as potential sources of references in the first step. We perform an extensive evaluation of our approach on datasets from two different domains -- Computer Science and Physics. For Computer Science we achieve a notably good performance with a precision@1 of 0.44 for reference recommendation as opposed to 0.38 obtained from the most competitive baseline. For the Physics dataset, we obtain a similar performance boost of 10% with respect to the most competitive baseline.
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