从人类导航轨迹中挖掘缺失的超链接：以维基百科为例
Mining Missing Hyperlinks from Human Navigation Traces: A Case Study of Wikipedia
摘要：
超链接是万维网的基本特征。它们对于维基百科这样的在线百科全书尤其重要：一篇文章通常只能在相关文章的上下文中理解，而超链接可以很容易地探索这种背景。但是重要的链接经常丢失，并且已经提出了几种方法来通过基于现有链接的结构学习链接模型来缓解这个问题。在这里，我们提出了一种新的方法来识别维基百科中的缺失链接。我们建立在维基百科链接的最终目的是帮助导航的事实上。我们的方法不是仅仅建议与现有链接结构相协调的新链接，而是找到缺失的链接，从而立即增强维基百科的可导航性。我们利用通过基于维基百科的人类计算游戏收集的导航路径数据集，在该游戏中，用户必须通过仅单击沿途遇到的链接找到从开始到目标文章的短路径。我们利用人类的导航轨迹来识别一组缺失链接的候选者，然后对这些候选者进行排名。实验表明，我们的程序识别高质量的缺失环节。
英文摘要：
Hyperlinks are an essential feature of the World Wide Web. They are especially important for online encyclopedias such as Wikipedia: an article can often only be understood in the context of related articles, and hyperlinks make it easy to explore this context. But important links are often missing, and several methods have been proposed to alleviate this problem by learning a linking model based on the structure of the existing links. Here we propose a novel approach to identifying missing links in Wikipedia. We build on the fact that the ultimate purpose of Wikipedia links is to aid navigation. Rather than merely suggesting new links that are in tune with the structure of existing links, our method finds missing links that would immediately enhance Wikipedia's navigability. We leverage data sets of navigation paths collected through a Wikipedia-based human-computation game in which users must find a short path from a start to a target article by only clicking links encountered along the way. We harness human navigational traces to identify a set of candidates for missing links and then rank these candidates. Experiments show that our procedure identifies missing links of high quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
