Research on statistical data visualization emphasizes the need for systems that assist in decision-making and visual analysis. Having found problems in chart construction by novice users, we decided to research the following question: How can we support novice users to create efficient visualizations with statistical data? Thus we, created ViSC, a recommender system that supports the interactive construction of charts to visualize statistical data by offering a series of recommendations based on the selected data and the user interaction with the tool. The system explores a visualization ontology to offer a set of graphs that help to answer information-based questions related to the current graph data. By traversing the recommended graphs through their related questions, the user implicitly acquires knowledge both on the domain and on visualization resources that better represent the domain concepts of interest. This dissertation presents the problems that motivated the research, describes the ViSC tool and presents the results of a qualitative study conducted to evaluate ViSC. We used two methods in our evaluation: the Semiotic Inspection Method (SIM) and the Retrospective Communicability Evaluation (RCE) — a combination of the Communicability Evaluation Method (CEM) and Retrospective Think Aloud Protocol. We first analyze how the questions influence the users traversal through the graph and, then, we address the broader question.