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Text mining to identify skills, stakeholders and capabilities: the case of Artificial Intelligence in Emilia-Romagna

Authors: Quinquillá, Arnau; Duran-Silva, Nicolau; Massucci, Francesco Alessandro; Fuster, Enric; Rondelli, Bernardo; Bologni, Leda; Mazzoni, Lucia; +1 Authors

Text mining to identify skills, stakeholders and capabilities: the case of Artificial Intelligence in Emilia-Romagna

Abstract

This is a poster presented at the 6th World Open Innovation Conference Objectives The study presents a semantic analysis to map the science, technology and innovation (STI) activities in the field of Artificial Intelligence in the Emilia-Romagna region (Italy). The study aims at fostering research-to-business collaborations; detecting existing and potential capabilities to address challenge-driven innovations. We identified which topics are covered, what is the relative regional specialisation, which are the key actors, and which are the internal and external linkages. Finally, we measured how the topics have been evolving in time and how the specific domain of Artificial intelligence is applied to solve societal challenges (SDGs). Methodology We analysed single STI records proceeding from different repositories. Text mining techniques were applied to the publication abstracts, patent descriptions and R&I project objectives to extract a wealth of textual information describing in detail STI activities and results. The methodology uses both automated text-mining computer techniques and qualitative and quantitative analysis.The approach is divided into the following steps: Extraction of documents produced by the regional research ecosystem; Definition of a vocabulary of key concepts that define the domain of research in Artificial intelligence Use of the vocabulary to identify, among the texts extracted in step 1, all those related to the domain of interest Fine-grained, unambiguous identification of the actors Quantitative analyses using the extracted data

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Keywords

Text mining, Artificial Intelligence, Research to Business, Sustainable development goals, R2B

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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