Ontological Tree Generation for Enhanced Information Retrieval
Information visualization seeks to leverage human visual processing to make sense of abstract information. One particularly rich class of information structures ripe for visualization are those representable as graphs (i.e. nodes and edges), including organization charts, website linkage, and computer networks. In this paper, the authors propose a methodology to extract information from big data and convert it into a human comprehensible format of graphs to give the reader an objective overall idea of the document content. They put forth the design and implementation details to mapping their data into the open directory project or the DMOZ tree and build a hierarchical ontological tree based on the extracted metadata.