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| An Approach to Identify Emerging Technologies Using Machine Learning: A Case Study of Robotics |
| Zhou Yuan1, Liu Yufei2, Xue Lan1 |
1. School of Public Policy and Management, Tsinghua University, Beijing 100084;
2. The Center for Strategic Studies, Chinese Academy of Engineering, Beijing 100036 |
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Abstract The traditional bibliometric method uses published articles and patents to improve the reliability and validity of technology foresight. It is a challenging task to extract information from massive datasets owing to limitations posed by manual feature extraction and encoding of knowledge. In addition, the lack of professional expertise leads to inefficient data analysis. In this work, we propose a disruptive technology foresight method based on topic model, which can improve the comprehensiveness and ensure consistent granularity of technology foresight via high throughput processing of massive text datasets. Further, the judgments of the expert group for the five key nodes of the machine learning algorithm improve the recognition abilities of this disruptive technology. In this study, the abstract, published time, and reference data in the Web of Science (WoS) and Thomson Innovation (TI) platforms are extracted to identify the relevant robot field clusters. The results provide useful support for further disruptive technology foresight work.
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Received: 02 November 2017
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