Sukhwan Jung, Rachana Reddy Kandadi, Rituparna Datta, Ryan Benton and Aviv Segev, University of South Alabama, USA

Technological developments are not isolated and are influenced not only by similar technologies but also by many entities, which are sometimes unforeseen by the experts in the field. The authors propose a method for identifying technology-relevant entities with trend curve analysis. The method first utilizes the tangential connection between terms in the encyclopedic dataset to extract technology-related entities with varying relation distances. Changes in their term frequencies within 389 million academic articles and 60 billion web pages are then analyzed to identify technology-relevant entities, incorporating the degrees and changes in both academic interests and public recognitions. The analysis is performed to find entities both significant and relevant to the technology of interest, resulting in the discovery of 40 and 39 technology-relevant entities, respectively, for unmanned aerial vehicle and hyperspectral imaging with 0.875 and 0.5385 accuracies. The case study showed the proposed method can capture hidden relationships between semantically distant entities.

Technology Forecasting, Trend Curve, Big Data, Academic Articles, Web Pages