A Literature Based Discovery Visualization System with Hierarchical Clustering and Linking Set Associations

Literature Based discovery (LBD) seeks to find information implicit in text, but never explicitly stated. In this work, we develop a method of visually summarizing LBD output in an automatically generated tree structure. This structure promotes a comprehensive understanding of LBD output as a whole, and encourages the user to explore branches of the hierarchy they find most interesting or surprising. This novel visualization system requires the development and integration of automatic functional group discovery, set associations, and linking set associations. Specifically, we perform hierarchical clustering on the potential discoveries generated by an LBD system to create a tree of potential hypotheses. We weight the tree by developing set association measures, and extending them to linking set association measures. This weighted tree is displayed in an interactive visual environment, and validated by replicating the historic Raynaud’s Disease – fish oil discovery