Papers written by researchers in medical and life sciences are a valuable source of information even for non-experts looking for knowledge related to rare diseases, but only if those non-experts can read English. If researchers create descriptors of their papers in the form of description logics (DL) ABoxes (assertion components) according to a DL ontology, then by using currently available software, computers can reason over the ABoxes to infer semantic consequences of the assertions in the descriptor. One open issue is how best to render information contained in the ABox for a particular user based on that user's knowledge requirements and background knowledge, including language preference. Natural language generation (NLG) is a method for rendering computer-interpretable statements, content models, in human-readable form, natural language text. ABoxes could be used as content models for NLG with particularly rich semantics. In particular, ABoxes could be used to generate expressions of expert knowledge in languages different from the original language that are more accurate and more tailor-fit to the user's cognitive state than existing methods for translating scientific papers. A method for generating natural language expressions from ABoxes in English and Japanese is presented and compared with a state-of-the-art expert translation software package.