While many visualization tools exist that offer sophisticated functions for charting complex data, they still expect users to possess a high degree of expertise in wielding the tools to create an effective visualization. This paper presents Articulate, an attempt at a semi-automated visual analytic model that is guided by a conversational user interface to allow users to verbally describe and then manipulate what they want to see. We use natural language processing and machine learning methods to translate the imprecise sentences into explicit expressions, and then apply a heuristic graph generation algorithm to create a suitable visualization. The goal is to relieve the user of the burden of having to learn a complex user-interface in order to craft a visualization.

Keywords: Visual analytics, natural language processing, conversational interface, automatic visualization.

PDF

Y. Sun, J. Leigh, A. Johnson, S. Lee, Articulate: A Semi-automated Model for Translating Natural Language Queries into Meaningful Visualizations, Proceddings of 10th International Symposium on Smart Graphics, Lecture Notes in Computer Science 2010, vol 6133, Banff, Canada, pp. 184-195, June 24th, 2010.