Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics

Academia and industry are constantly engaged in a joint effort for producing scientific knowledge that will shape the society of the future. Analysing the knowledge flow between them and understanding how they influence each other is a critical task for researchers, governments, funding bodies, investors, and companies. However, current corpora are unfit to support large-scale analysis of the knowledge flow between academia and industry since they lack of a good characterization of research topics and industrial sectors. In this short paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which characterizes 14M papers and 8M patents according to the research topics drawn from the Computer Science Ontology. 4M papers and 5M patents are also classified according to the type of the author's affiliations (academy, industry, or collaborative) and 66 industrial sectors (e.g., automotive, financial, energy, electronics) obtained from DBpedia. AIDA was generated by an automatic pipeline that integrates several knowledge graphs and bibliographic corpora, including Microsoft Academic Graph, Dimensions, English DBpedia, the Computer Science Ontology, and the Global Research Identifier Database

ResearchFlow: Understanding the Knowledge Flow between Academia and Industry

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

Innovation capacity in the healthcare sector and historical anchors: examples from the UK, Switzerland and the US

Innovation is an integral part of economic development in developed economies. In the post 2008 period, a key policy agenda is that of sustainable development, which calls for innovation in all aspects of value-chains. In this paper, we focus on innovation from the biotech—pharma perspective to see whether or not this will lead to a sustainable future for the regions where there are clusters of firms in this sector. We examine data from a recently completed European Union study of innovation in the Healthcare sector from the UK and Switzerland, countries with an historical base in pharma, to understand how innovation pathways vary at the regional level in the broader life sciences, which incorporate biotech and more. Innovation in the healthcare sector in two regions, Oxfordshire in the UK and Zurich in Switzerland are compared. We contextualize our discussion by drawing on studies that focus on the sector in the US, specifically Boston. The analytical framework comprises three elements: innovation systems and national and regional economic development theories are the first two, followed by approaches which consider organizational or institutional activity. This framework is used to help explain and understand the complexity of how innovation is organized at the sub-national level. The overall context is that it is increasing becoming a condition for government financing of research that it has more immediate application in industry or have the possibility of commercialisation (e.g., translational research)

Engagement Through Communication:Communicating Scientific Knowledge to SMEs

In this chapter, we work from the assumption that university engagement can be fostered by addressing the dissemination of scientific knowledge as a communication process, and we explore how university engagement can be encouraged through the communication of scientific knowledge to SMEs (small and mediumsized enterprises). First, a literature review allows for the identification of wellknown barriers to the dissemination of scientific knowledge to SMEs. Second, an empirical study of the ‘situation’ of eight Danish SMEs provides insights into their situation (circumstances, barriers and potentials) in relation to scientific knowledge, which must be taken into account in attempts to communicate scientific knowledge to SMEs. Based on this analysis, we discuss solutions and outline some communicative principles that can contribute with a solution-oriented perspective on how communicating scientific knowledge to SMEs can foster university engagement.</p