Detecting technological maturity from bibliometric patterns

Cauthen, Katherine; Rai, Prashant; Hale, Nicholas; Freeman, Laura; Ray, Jaideep

doi:10.1016/j.eswa.2022.117177

Detecting technological maturity from bibliometric patterns

Journal Article · Fri Apr 08 00:00:00 EDT 2022 · Expert Systems with Applications

DOI:https://doi.org/10.1016/j.eswa.2022.117177· OSTI ID:1870490

Cauthen, Katherine ^[1]; Rai, Prashant ^[2]; Hale, Nicholas ^[3]; Freeman, Laura ^[3]; Ray, Jaideep ^[2]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Virginia Tech Applied Research Corporation, Arlington, VA (United States)

We report the capability to identify emergent technologies based upon easily accessed open-source indicators, such as publications, is important for decision-makers in industry and government. The scientific contribution of this work is the proposition of a machine learning approach to the detection of the maturity of emerging technologies based on publication counts. Time-series of publication counts have universal features that distinguish emerging and growing technologies. We train an artificial neural network classifier, a supervised machine learning algorithm, upon these features to predict the maturity (emergent vs. growth) of an arbitrary technology. With a training set comprised of 22 technologies we obtain a classification accuracy ranging from 58.3% to 100% with an average accuracy of 84.6% for six test technologies. To enhance classifier performance, we augmented the training corpus with synthetic time-series technology life cycle curves, formed by calculating weighted averages of curves in the original training set. Training the classifier on the synthetic data set resulted in improved accuracy, ranging from 83.3% to 100% with an average accuracy of 90.4% for the test technologies. The performance of our classifier exceeds that of competing machine learning approaches in the literature, which report an average classification accuracy of only 85.7% at maximum. Moreover, in contrast to current methods our approach does not require subject matter expertise to generate training labels, and it can be automated and scaled.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-CA), Livermore, CA (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: Army Research Laboratory; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 1870490

Alternate ID(s):: OSTI ID: 1960895

Report Number(s):: SAND2022-4746J; 705120

Journal Information:: Expert Systems with Applications, Journal Name: Expert Systems with Applications Vol. 201; ISSN 0957-4174

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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artificial neural network
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Detecting technological maturity from bibliometric patterns

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