| 71665 |
Creator |
c9aa7f2e582d191ed728ad414c5ea711 |
| 71665 |
Creator |
21e3abf33e3daaa89c07ea7d5da24bb0 |
| 71665 |
Creator |
25b3b10b9da03c08922eae28b1249552 |
| 71665 |
Date |
2020 |
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Is Part Of |
repository |
| 71665 |
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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authorList |
authors |
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editorList |
editors |
| 71665 |
presentedAt |
ext-ca75aa039e891029d6ff4d079315bf65 |
| 71665 |
status |
peerReviewed |
| 71665 |
uri |
http://data.open.ac.uk/oro/document/1197785 |
| 71665 |
uri |
http://data.open.ac.uk/oro/document/1197787 |
| 71665 |
uri |
http://data.open.ac.uk/oro/document/1197788 |
| 71665 |
uri |
http://data.open.ac.uk/oro/document/1197789 |
| 71665 |
uri |
http://data.open.ac.uk/oro/document/1197790 |
| 71665 |
uri |
http://data.open.ac.uk/oro/document/1197791 |
| 71665 |
uri |
http://data.open.ac.uk/oro/document/1200213 |
| 71665 |
type |
AcademicArticle |
| 71665 |
type |
Article |
| 71665 |
label |
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.), Springer, (In Press). |
| 71665 |
Publisher |
ext-1c5ddec173ca8cdfba8b274309638579 |
| 71665 |
Title |
ResearchFlow: Understanding the Knowledge Flow between Academia and Industry |
| 71665 |
in dataset |
oro |