Towards Finding Relevant Information Graphics: Identifying the Independent and Dependent Axis from User-Written Queries

Information graphics (non-pictorial graphics such as bar charts and line graphs) contain a great deal of knowledge. Information retrieval research has focused on retrieving textual documents and on extracting images based on words appearing in the accompanying article or based on low-level features such as color or texture. Our goal is to build a system for retrieving information graphics that reasons about the content of the graphic itself in deciding its relevance to the user query. As a first step, we aim to identify, from a full sentence user query, what should be depicted on the independent and dependent axes of potentially relevant graphs. Natural language processing techniques are used to extract features from the query and machine learning is employed to build a model for hypothesizing the content of the axes. Results have shown that our models can achieve accuracy higher than 80% on a corpus of collected user