Wikipedia is a goldmine of information. Each article describes a single concept, and together they constitute a vast investment of manual effort and judgment. Wikification is the process of automatically augmenting a plain-text document with hyperlinks to Wikipedia articles. This involves associating phrases in the document with concepts, disambiguating them, and selecting the most pertinent. All three processes can be addressed by exploiting Wikipedia as a source of data. For the first, link anchor text illustrates how concepts are described in running text. For the second and third, Wikipedia provides millions of examples that can be used to prime machine-learned algorithms for disambiguation and selection respectively. Wikification produces a semantic representation of any document in terms of concepts. We apply this to (a) select index terms for scientific documents, and (b) determine the similarity of two documents, in both cases outperforming humans in terms of agreement with human judgment. I will show how it can be applied to document clustering and classification algorithms, and to produce back of the book indexes, improving on the state of the art in each case.