Open innovation is a new paradigm embraced by companies to introduce transformations. It assumes that firms can and should use external and internal ideas to innovate. Recently, commercial and research projects have undergone an exponential growth, leading the open challenge of identifying possible insights on interesting aspects to work on. The existing literature has focused on the identification of goals, topics, and keywords in a single piece of text. However, insights do not have a clear structure and cannot be validated by comparing them with a straightforward ground truth, thus making their identification particularly challenging. Besides the extraction of insights from previously existing initiatives, the issue of how to present them to a company in a ranking also emerges. To overcome these two issues, we present an approach that extracts insights from a large number of projects belonging to distinct domains, by analyzing their abstract. Then, our method is able to rank these results, to support project preparation, by presenting first the most relevant and timely/recent insights. Our evaluation on real data coming from all the Horizon 2020 European projects, shows the effectiveness of our approach in a concrete case study.

A Text Mining Approach to Extract and Rank Innovation Insights from Research Projects

Malloci F. M.;Boratto L.;Fenu G.
2020

Abstract

Open innovation is a new paradigm embraced by companies to introduce transformations. It assumes that firms can and should use external and internal ideas to innovate. Recently, commercial and research projects have undergone an exponential growth, leading the open challenge of identifying possible insights on interesting aspects to work on. The existing literature has focused on the identification of goals, topics, and keywords in a single piece of text. However, insights do not have a clear structure and cannot be validated by comparing them with a straightforward ground truth, thus making their identification particularly challenging. Besides the extraction of insights from previously existing initiatives, the issue of how to present them to a company in a ranking also emerges. To overcome these two issues, we present an approach that extracts insights from a large number of projects belonging to distinct domains, by analyzing their abstract. Then, our method is able to rank these results, to support project preparation, by presenting first the most relevant and timely/recent insights. Our evaluation on real data coming from all the Horizon 2020 European projects, shows the effectiveness of our approach in a concrete case study.
978-3-030-62007-3
978-3-030-62008-0
Information extraction; Ranking; Text mining
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/322931
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