Natural language interfaces to databases (NLIDB) are systems that aim to bridge the gap between the languages used by humans and computers, and automatically translate natural language sentences to database queries. This thesis proposes a novel approach to NLIDB, using graph-based models. The system starts by collecting as much information as possible from existing databases and sentences, and transforms this information into a knowledge base for the system. Given a new question, the system will use this knowledge to analyze and translate the sentence into its corresponding database query statement. The graph-based NLIDB system uses English as the natural language, a relational database model, and SQL as the formal query language. In experiments performed with natural language questions ran against a large database containing information about U.S. geography, the system showed good performance compared to the state-of-the-art in the field.