The Wikipedia is a web-based encyclopedia, written and edited collaboratively by Internet users. The Wikipedia has an extremely open editorial policy that allows anybody, to create or modify articles. This has promoted a broad and detailed coverage of subjects, but also introduced problems relating to the quality of articles. The Wikipedia Recommender System (WRS) was developed to help users determine the credibility of articles based on feedback from other Wikipedia users. The WRS implements a collaborative filtering system with trust metrics, i.e., it provides a rating of articles which emphasizes feedback from recommenders that the user has agreed with in the past. This exposes the problem that most recommenders are not equally competent in all subject areas. The first WRS prototype did not include an evaluation of the areas of expertise of recommenders, so the trust metric used in the article ratings reflected the average competence of recommenders across all subject areas. We have now developed a new version of the WRS, which evaluates the expertise of recommenders within different subject areas. In order to do this, we need to identify a way to classify the subject area of all the articles in the Wikipedia. In this paper, we examine different ways to classify the subject area of Wikipedia article according to well established knowledge classification schemes. We identify a number of requirements that a classification scheme must meet in order to be useful in the context of the WRS and present an evaluation of four existing knowledge classification schemes with respect to these requirements. This evaluation helped us identify a classification scheme, which we have implemented in the current version of the Wikipedia Recommender System.