Rating the similarity of two or more text documents is an essential task in information retrieval. For example, document similarity can be used to rank search engine results, cluster documents according to topics etc. A major challenge in calculating document similarity originates from the fact that two documents can have the same topic or even mean the same, while they use different wording to describe the content. A sophisticated algorithm therefore will not directly operate on the texts but will have to find a more abstract representation that captures the texts' meaning. In this paper, we propose a novel approach for calculating the similarity of text documents. It builds on conceptual contexts that are derived from content and structure of the Wikipedia hypertext corpus.