Mapping Complex Technologies via Science-Technology Linkages; The Case of Neuroscience

Dr. Daniel Hain, Aalborg University Business School

In this seminar, Dr. Daniel Hain will present an efficient deep learning based approach to extract technology-related topics and keywords within scientific literature, and identify corresponding technologies within patent applications. Specifically, this study utilizes transformer based language models, tailored for use with scientific text, to detect coherent topics over time and describe these by relevant keywords that are automatically extracted from a large text corpus. The authors create a large amount of search queries based on combinations of method and application-keywords, which they use to conduct semantic search and identify related patents. By doing so, they aim at contributing to the growing body of research on text-based technology mapping and forecasting that leverages latest advances in natural language processing and deep learning. This study maps technologies identified in scientific literature to patent applications, thereby providing an empirical foundation for the study of science-technology linkages. It also illustrates the workflow as well as results obtained by mapping publications within the field of neuroscience to related patent applications.

About the speaker

Daniel Hain is an Associate Professor in Innovation Economics & Data Science at the Aalborg University Business School. His research is dedicated to the development and application of data-driven methods to map, understand, and predict technological change, and its causes and consequences for socioeconomic systems on various levels of aggregation. His current contextual focus is the dynamics of AI research and industry. Daniel is actively engaged in initiatives to educate (social science) students, professionals, and policymakers in understanding, evaluating, and applying modern Data Science and Artificial Intelligence methods for data-driven decision making.

Venue: Room 0.18 UNU MERIT; or via Zoom (please contact us at for the Zoom link)

Date: 25 April 2022

Time: 12:00 - 13:00  CEST