ResearchFlow: Understanding the Knowledge Flow Between Academia and Industry

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.

Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain

“…The combination of organization types and topics in AIDA allows researchers to produce several kinds of advanced analysis. For instance, it was recently used to improve the state of the art regarding the prediction of research impact on the industrial sectors [41].…”

“…This knowledge base enables tracking the evolution of research topics across academia, industry, government institutions, and other organizations. For instance, it was recently used for predicting the impact of specific research efforts on the industrial sector [41]. However, out of the 21M articles, only 5.1M were linked with GRID IDs in the source data and thus could be associated to their affiliation types and countries.…”

Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs

“…In describing this framework we leverage and reorganise our previous research works. Specifically, we discuss the advantage of a solution based on a formal representation of research topics and describe how it was applied to produce bibliometrics studies [34,47] and novel approaches for analysing [56,66,2] and predicting [66,70,52] research dynamics.…”

“…the EDAM methodology [56] for supporting systematic reviews (Section 4.1); the ResearchFlow framework [66] for analysing trends across academia and industry (Section 4.2); the AIDA Dashboard [2] for supporting trend analysis in scientific conferences (Section 4.3).…”

“…-Augur [70] (Section 5.1), a method for predicting the emergence of novel research topics; the ResearchFlow forecaster [66] (Section 5.2), a supervised deep learning classifier for predicting the impact of research topics on the industrial sector; the Technology-Topic Framework (TTF) [52] (Section 5.3), an approach for predicting the technologies that will be adopted by a research field.…”