We focus on the may_treat predicate linking drugs and disorders. Such predicate is expressed in RDF format in the VHA National Drug File Reference Terminology (NDF-RT), a specialized medical resource. The DailyMed dataset also contains this predicate, but only in textual form: for each drug there is an indication field that links the drug’s URI to a literal that is a long description (more than 100 words on the average). We show that natural language processing (NLP) techniques can be used to further distil the indication field to extract may_treat predicates. We then move to Web exploration and show how we can apply similar NLP techniques to find may_treat predicates. The diversity in natural language expressions and the embedding of good information among noisy and often redundant data make it a challenge to exploit the Web.