Backed by major Web players, schema.org is the latest broad initiative for structuring Web information. Unfortunately, extensive analysis on a
large corpus of 733 million web documents shows that, a year after its introduction, only 1.56% of the documents had schema.org annotations. The reason is that the annotation process is quite demanding, hindering wide-spread adoption. State-of-the-art tools like Google’s Structured Data Markup Helper offer only limited support. In this paper we propose SASS, a system for automatically finding high quality schema suggestions for page content, to ease up the annotation process. For this purpose the system employs supervised machine learning techniques together with user feedback. Additional support for binding attributes to values reduces the effort even further. These features make SASS superior to other tools offering support for schema.org annotations up to date.