GlassViz: Visualizing Automatically-Extracted Entry Points for Exploring Scientific Corpora in Problem-Driven Visualization Research
This work addresses the problem of efficiently navigating scientific literature for domain and visualization experts, though it appears incremental as it builds on existing methods for text analysis and visualization.
The paper tackled the challenge of document discovery in problem-driven visualization research by developing a model and visual text analytics tool that uses keyword associations from disjoint paper collections to create entry points for exploring large corpora, demonstrated in the digital humanities context.
In this paper, we report the development of a model and a proof-of-concept visual text analytics (VTA) tool to enhance documentdiscovery in a problem-driven visualization research (PDVR) con-text. The proposed model captures the cognitive model followed bydomain and visualization experts by analyzing the interdisciplinarycommunication channel as represented by keywords found in twodisjoint collections of research papers. High distributional inter-collection similarities are employed to build informative keywordassociations that serve as entry points to drive the exploration of alarge document corpus. Our approach is demonstrated in the contextof research on visualization for the digital humanities.