Abstract : Robust, statistical Natural Language Generation from Web knowledge bases is hindered by the lack of text-aligned resources. We aim to fill this gap by presenting a method for extracting knowledge from natural language text, and encode it in a format based on frame semantics and ready to be distributed in the Linked Open Data space. We run an implementation of such methodology on a collection of short documents and build a repository of frame instances equipped with fine-grained lex-icalizations. Finally, we conduct a pilot stody to investigate the feasibility of an approach to NLG based on said resource. We perform error analysis to assess the quality of the resource and manually evaluate the output of the NLG prototype.