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

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Book cover Web Information Systems Engineering – WISE 2020 (WISE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12343))

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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.

All authors equally contributed to this research.

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Notes

  1. 1.

    https://spacy.io/.

  2. 2.

    https://spacy.io/.

  3. 3.

    https://www.elastic.co/.

  4. 4.

    https://data.europa.eu/euodp/en/data/dataset/cordisref-data.

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Author information

Authors and Affiliations

  1. Data Science and Big Data Analytics Unit, EURECAT - Centre Tecnògic de Catalunya, Carrer de Bilbao 72, 08005, Barcelona, Spain

    Laura Portell Penadés & Ludovico Boratto

  2. Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124, Cagliari, Italy

    Francesca Maridina Malloci & Gianni Fenu

Authors
  1. Francesca Maridina Malloci
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  2. Laura Portell Penadés
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  3. Ludovico Boratto
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  4. Gianni Fenu
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Corresponding author

Correspondence to Francesca Maridina Malloci .

Editor information

Editors and Affiliations

  1. VU Amsterdam, Amsterdam, The Netherlands

    Zhisheng Huang

  2. VU Amsterdam, Amsterdam, The Netherlands

    Wouter Beek

  3. Victoria University, Melbourne, VIC, Australia

    Hua Wang

  4. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  5. Victoria University, Melbourne, VIC, Australia

    Yanchun Zhang

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Malloci, F.M., Penadés, L.P., Boratto, L., Fenu, G. (2020). A Text Mining Approach to Extract and Rank Innovation Insights from Research Projects. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12343. Springer, Cham. https://doi.org/10.1007/978-3-030-62008-0_10

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  • DOI: https://doi.org/10.1007/978-3-030-62008-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62007-3

  • Online ISBN: 978-3-030-62008-0

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