Abstract
Small and medium enterprises (SMEs) are a driving force for innovation. Stimulation of innovation in these SMEs is often the target of policy interventions, both regionally and nationally. Which technical areas should be in the focus and how to identify and monitor them? In this position paper, we propose hybrid AI methods for innovation monitoring, using natural language processing (NLP) and a dynamic knowledge graph that combines learning, reasoning and knowledge sharing in collaboration with innovation experts.
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Geurts, A. et al.: Data Supported Foresight. Creating a new foundation for future anticipation by leveraging the power of AI and Big Data to go beyond current practice. TNO/Frauenhofer ISI whitepaper (2020)
Geurts, A.: A critical review of Alex Ross’s the industries of the future. Technol. Forecast. Soc. Chang. 128(1), 311–313 (2018)
Utterback, J.M.: Mastering the Dynamics of Innovation: How Companies Can Seize Opportunities in the Face of Technological Change. Harvard University Press, Cambridge (1994)
Cozzens, S., et al.: Emerging technologies: quantitative identification and measurement. Technol. Anal. Strateg. Manag. 22, 361–376 (2010)
Mühlroth, C., Grottke, M.: Artificial intelligence in innovation: how to spot emerging trends and technologies. IEEE Trans. Eng. Manag. 1–18 (2020)
Himanen, L., Geurts, A., Foster, A., Rinke, P.: Data-driven materials science: status, challenges, perspectives. Adv. Sci. 6(21), 1900808 (2019)
Smith, B.: Ontology. In: Floridi, L. (ed.) Blackwell Guide to the Philosophy of Computing and Information, pp. 155–166. Wiley, New York (2003)
Zijderveld, E.J.A.: MARVEL - principles of a method for semi-qualitative system behaviour and policy analysis. TNO paper (2007)
Raaijmakers et al.: AI-supported Foresight and bias: towards a hybrid approach. TNO working paper (2020)
Daas, P.J.H., van der Doef, S.: Detecting innovative companies via their website. Stat. J. IAOS 36(4), 1239–1251 (2020)
Daas, P.J.H., Jansen, J.: Model degradation in web derived text-based models. In: International Conference on Advanced Research Methods and Analytics (CARMA), Valencia, Spain (2020). Accepted for publication
Kullback, S.: Letter to the editor: the Kullback-Leibler distance. Am. Stat. 41(4), 340–341 (1987)
Kinne, J., Lenz, D.: Predicting innovative firms using web mining and deep learning. ZEW Discussion paper no 19-001, Mannheim, Germany (2019)
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Braaksma, B., Daas, P., Raaijmakers, S., Geurts, A., Meyer-Vitali, A. (2021). AI-Supported Innovation Monitoring. In: Heintz, F., Milano, M., O'Sullivan, B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science(), vol 12641. Springer, Cham. https://doi.org/10.1007/978-3-030-73959-1_20
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DOI: https://doi.org/10.1007/978-3-030-73959-1_20
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