TPACTechnology Policy and Assessment Center
 
 

Automated extraction and visualization of information for technological intelligence and forecasting

Donghua Zhua and Alan L. Porter
appeared in Technological Forecasting & Social Change

Abstract: Empirical technology forecasting (TF) is not well utilized in technology management. Three factors could enhance managerial utilization: capability to exploit huge volumes of available information, ways to do so very quickly, and informative representations that help manage emerging technologies. This paper reports on efforts to address these three factors via partially automated processes to generate helpful knowledge from text quickly and graphically. We first illustrate a process to generate a family of technology maps that help convey emphases, players, and patterns in the development of a target technology. Second, we exemplify the generation of particular ‘‘innovation indicators’’ that measure particular facets of R&D activity to relate these to technological maturation, contextual influences, and market potential. Both technology mapping and innovation indicators rely upon searches in huge, easily accessible, abstract databases and text mining software. We augment these through ‘‘macros’’ (programming scripts) that automatically sequence the necessary steps to generate particular desired information products. These analytical findings can be tailored to the needs of particular technology managers.

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