As the rapid rise of information stored in document databases continues, there is a real possibility of using these textual databases in systems that automatically provide answers to questions issued by users in natural language. Identification of candidate answers, within these documents, using Question Answering systems, is a challenging task that has been tackled by many researchers. One of the main problems identified in this domain is to retrieve text passages that potentially contain answers to the questions. Another problem is the extraction of text excerpts that are highly likely to answer the questions. This book discusses the work on utilizing Frame Semantics encapsulated in FrameNet to enhance the performance of semantic QA systems. FrameNet provides scenario-based generalizations over lexical items that share similar semantic backgrounds. This work also addresses some other challenges that influence frame semantic-based QA effectiveness. This includes the level of frame semantic parsing of texts, lexical coverage in FrameNet, and methods to fuse a FrameNet-based answer processing model with a non-semantic model.