  A major challenge for many analyses of Wikipedia dynamics -- e.g., imbalances
in content quality, geographic differences in what content is popular, what
types of articles attract more editor discussion -- is grouping the very
diverse range of Wikipedia articles into coherent, consistent topics. This
problem has been addressed using various approaches based on Wikipedia's
category network, WikiProjects, and external taxonomies. However, these
approaches have always been limited in their coverage: typically, only a small
subset of articles can be classified, or the method cannot be applied across
(the more than 300) languages on Wikipedia. In this paper, we propose a
language-agnostic approach based on the links in an article for classifying
articles into a taxonomy of topics that can be easily applied to (almost) any
language and article on Wikipedia. We show that it matches the performance of a
language-dependent approach while being simpler and having much greater
coverage.
