Abstract:
Identifying emerging technologies and forecasting their trends is pivotal for stakeholders and decision-makers across academia, industry, and government agencies. The current strategies employed to track technology trends often rely on proprietary closed datasets and often rely on the insights of human domain experts. Not only are these approaches expensive and manual, but they are also time-consuming. In this study, we introduce an automated method for identifying emerging trends through a quantitative approach that utilizes extensive publicly available data, including patents, publications, and Wikipedia Pageview statistics. Our method proposes four criteria – novelty, growth, impact, and coherence – to automatically score technologies, based on a mathematical foundation. This approach enables the monitoring of tech trends across various sectors in an automated manner, without the need for domain experts. The results obtained through rigorous evaluation, benchmarked against similar reports from leading market research firms, illustrate a low recall rate paired with high precision, affirming the reliability of our proposed method. Furthermore, our method identifies emerging technologies not present in similar market reports, highlighting its unique capabilities.
Research Paper:
articleBibTex:
@article{dolamic2023automatedIdentification,
title = {Automated Identification of Emerging Technologies: OpenData Approach},
author={Dolamic, Ljiljana and Jang-Jaccard, Julian and Mermoud, Alain and Lenders, Vincent},
journal = {Joint Workshop of the 5th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2024) and the 4th AI + Informetrics (AII2024)},
}