Predicting Links on Wikipedia with Anchor Text Information
This work addresses link prediction for Wikipedia editors and readers, but it is incremental as it focuses on baseline comparisons without introducing major innovations.
The paper tackled the problem of predicting links on Wikipedia using anchor text information, proposing an evaluation methodology and baseline models to estimate task difficulty.
Wikipedia, the largest open-collaborative online encyclopedia, is a corpus of documents bound together by internal hyperlinks. These links form the building blocks of a large network whose structure contains important information on the concepts covered in this encyclopedia. The presence of a link between two articles, materialised by an anchor text in the source page pointing to the target page, can increase readers' understanding of a topic. However, the process of linking follows specific editorial rules to avoid both under-linking and over-linking. In this paper, we study the transductive and the inductive tasks of link prediction on several subsets of the English Wikipedia and identify some key challenges behind automatic linking based on anchor text information. We propose an appropriate evaluation sampling methodology and compare several algorithms. Moreover, we propose baseline models that provide a good estimation of the overall difficulty of the tasks.