Machine-learning-based deep semantic analysis approach for forecasting new technology convergence

Tae San Kim and So Young Sohn

Technological Forecasting and Social Change, 2020, vol. 157, issue C

Abstract: Technology convergence is extremely important for creating novel value and introducing new products and services. Recently, a fluctuating and competitive environment has prompted radical technology fusions. Although many frameworks were suggested for predicting convergence, it was not easy to forecast fusion between new technologies. To overcome this issue, we propose a machine-learning-based framework that uses semantic analysis along with traditional methods such as link prediction and bibliometric analysis to identify convergence patterns. We exploit text information of patent for semantic analysis, which is time-invariant and useful for identifying semantic patterns of convergence. In particular, the document to vector method is used to identify the semantic relevance of technologies. We apply our framework to the convergence technology fields of (1) motor vehicles and (2) signal transmission and telecommunications. The results show that consideration of text information increases the performance for the prediction of new convergence.

Keywords: Technology convergence; Link prediction analysis; Patent analysis; Semantic analysis; Text mining (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:157:y:2020:i:c:s0040162520309215

DOI: 10.1016/j.techfore.2020.120095

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