Privacy policies are intended to inform users about the collection and use of their data by websites, mobile apps and other services or appliances they interact with. This also includes informing users about any choices they might have regarding such data practices. However, few users read these often long privacy policies; and those who do have difficulty understanding them, because they are written in convoluted and ambiguous language. A promising approach to help overcome this situation revolves around semi-automatically annotating policies, using combinations of crowdsourcing, machine learning and natural language processing. In this article, we introduce PrivOnto, a semantic framework to represent annotated privacy policies. PrivOnto relies on an ontology developed to represent issues identified as critical to users and/or legal experts. PrivOnto has been used to analyze a corpus of over 23,000 annotated data practices, extracted from 115 privacy policies.