ResearchFlow: Understanding the Knowledge Flow between Academia and Industry

Salatino, Angelo; Osborne, Francesco and Motta, Enrico (2020). ResearchFlow: Understanding the Knowledge Flow between Academia and Industry. In: Proceedings of the 22nd International Conference on Knowledge Engineering and Knowledge Management (Keet, C. Maria and Dumontier, Michel eds.), Lecture Notes in Computer Science vol 12387, Springer, pp. 219–236.

DOI: https://doi.org/10.1007/978-3-030-61244-3_16

Abstract

Understanding, monitoring, and predicting the flow of knowledge between academia and industry is of critical importance for a variety of stakeholders, including governments, funding bodies, researchers, investors, and companies. To this purpose, we introduce ResearchFlow, an approach that integrates semantic technologies and machine learning to quantifying the diachronic behaviour of research topics across academia and industry. ResearchFlow exploits the novel Academia/Industry DynAmics (AIDA) Knowledge Graph in order to characterize each topic according to the frequency in time of the related i) publications from academia, ii) publications from industry, iii) patents from academia, and iv) patents from industry. This representation is then used to produce several analytics regarding the academia/industry knowledge flow and to forecast the impact of research topics on industry. We applied ResearchFlow to a dataset of 3.5M papers and 2M patents in Computer Science and highlighted several interesting patterns. We found that 89.8% of the topics first emerge in academic publications, which typically precede industrial publications by about 5.6 years and industrial patents by about 6.6 years. However this does not mean that academia always dictates the research agenda. In fact, our analysis also shows that industrial trends tend to influence academia more than academic trends affect industry. We evaluated ResearchFlow on the task of forecasting the impact of research topics on the industrial sector and found that its granular characterization of topics improves significantly the performance with respect to alternative solutions.

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