The amount of data that is available to us is ever increasing, and thus is the potential to extract information from it. Information visualization, which leverages our perceptual system to enable us to perceive patterns, outliers, trends and anomalies in large amounts of data, is an important technique for exploratory data analysis. As part of a flexible visual data analysis process, the user needs to construct and parametrize visualizations, which is challenging for novice users. In this thesis, I explore how information visualization novices can be supported in visualization construction. First, I identify existing visualization construction approaches in a systematic literature survey and examine their use cases. Second, I conduct a laboratory study to learn about the process and the characteristics of how information visualization novices construct visualization during data analysis. Third, I identify natural language visualization queries as a promising alternative specification approach that I study by analyzing the queries from the laboratory experiment and by conducting an online survey study. Based on my findings, I propose a descriptive model of natural language visualization queries. Fourth, I derive guidelines for visualization construction tools from my studies and from related work. Finally, I show how these guidelines can be applied to existing visualization tools using the example of the Choosel visualization framework. / Graduate