Creating and exploiting aggregate information from text using a novel Categorized Document Base abstract

FIGURES

Figure 1: Results produced by exploratory search on ‘fishing’ using clusty.com

Figure 2: Results produced by exploratory search on ‘fishing’ using kartoo.com

Figure 3: Results produced by exploratory search on ‘US locations fishing’ using clusty.com

Figure 4: Hits for ‘fishing’ by state, from a CDB populated with American cities

Figure 5: Population of categories with relevant documents

Figure 6: Screen from our software prototype, showing word suggestion tool to allow user to select an expansion or disambiguation of the current search term

Figure 7: Computing hits for each category

Figure 8: Relative prevalence of the terms “smoothness”, “strength” and [“wet” or “damp”] in various stone quarrying industry segments. Three bars are shown for each industry: from left to right, the three bars for that industry are “smoothness” for that industry, “strength” for that industry, and [“wet OR damp”] for that industry.

Figure 9: Excel-based collapsible tree view provided for the exploration of

Figure 10: Web-based collapsible tree view provided for the exploration of

Figure 11: Collaborative annotation interface for sharing of human observations on interesting categories amongst a team: illustration of users sharing comments on possible applications of a biodegradable molecule with foam reduction properties.

Figure 15: Bubble Chart Showing Integration of

Figure 16: Sample of publicly traded companies in the plastics packaging industry, obtained from the SEC, after a CDB exploration revealed that documents in the plastics packaging industry frequently mention a compound being marketed by a chemical industry salesperson.