Wikipedia-based studies and systems frequently assume that no two articles describe the same concept. However, in this paper, we show that this article-as-concept assumption is problematic due to editors' tendency to split articles into parent articles and sub-articles when articles get too long for readers (e.g. "Portland, Oregon" and "History of Portland, Oregon" in the English Wikipedia). In this paper, we present evidence that this issue can have significant impacts on Wikipedia-based studies and systems and introduce the subarticle matching problem. The goal of the sub-article matching problem is to automatically connect sub-articles to parent articles to help Wikipedia-based studies and systems retrieve complete information about a concept. We then describe the first system to address the sub-article matching problem. We show that, using a diverse feature set and standard machine learning techniques, our system can achieve good performance on most of our ground truth datasets, significantly outperforming baseline approaches.