We propose a novel methodology for retrieving infographics in response to
a user query. Our approach analyzes the user query to hypothesize the desired
content of the independent and dependent axes of relevant infographics and the
high-level message that a relevant infographic should convey. It then ranks candidate graphics using a mixture model that takes into account the textual content
of the graphic, the relevance of its axes to the structural content requested in the
user query, and the relevance of the graphic's intended message to the information need (such as a comparison) identified from the user's query. We currently
focus on static simple bar charts and line graphs; in the future, our methodology
will be extended to more complex infographics.