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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
Text mining, Artificial Intelligence, Research to Business, Sustainable development goals, R2B
Text mining, Artificial Intelligence, Research to Business, Sustainable development goals, R2B
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| 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. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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