This paper presents a method for the generation of struc- tured data sources for music recommendation using information extracted from unstructured text sources. The proposed method identi es entities in text that are relevant to the music domain, and then extracts seman- tically meaningful relations between them. The extracted entities and re- lations are represented as a graph, from which the recommendations are computed. A major advantage of this approach is that the recommenda- tions can be conveyed to the user using natural language, thus providing an enhanced user experience. We test our method on texts from song- facts.com, a website that provides facts and stories about songs. The extracted relations are evaluated intrinsically by assessing their linguis- tic quality, as well as extrinsically by assessing the extent to which they map an existing music knowledge base. Finally, an experiment with real users is performed to assess the suitability of the extracted knowledge for music recommendation. Our method is able to extract relations between pair of musical entities with high precision, and the explanation of those relations to the user improves user satisfaction considerably.