This paper describes and evaluates a method for computing artist similarity from a set of artist biographies. The/nproposed method aims at leveraging semantic information present in these biographies, and can be divided in three/nmain steps, namely: (1) entity linking, i.e. detecting mentions to named entities in the text and linking them to an/nexternal knowledge base; (2) deriving a knowledge representation from these mentions in the form of a semantic/ngraph or a mapping to a vector-space model; and (3) computing semantic similarity between documents. We/ntest this approach on a corpus of 188 artist biographies and a slightly larger dataset of 2,336 artists, both gathered/nfrom Last.fm. The former is mapped to the MIREX Audio and Music Similarity evaluation dataset, so that its similarity/njudgments can be used as ground truth. For the latter dataset we use the similarity between artists as provided/nby the Last.fm API. Our evaluation results show that an approach that computes similarity over a graph of entitiesand semantic categories clearly outperforms a baseline that exploits word co-occurrences and latent factors.