Artificial intelligence approach for linking competences in nuclear field
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Nuclear Engineering and Technology, Volume 56, issue 1, pp. 340-356
Abstract
Bridging traditional experts’ disciplinary boundaries is important for nuclear knowledge management systems. However, expert competences are often described in unstructured texts and require substantial human effort to link related competences across disciplines. The purpose of this research is to develop and evaluate a natural language processing approach, based on Latent Semantic Analysis, to enable the automatic linking of related competences across different disciplines and communities of practice. With datasets of unstructured texts as input training data, our results show that the algorithm can readily identify nuclear domain-specific semantic links between words and concepts. We discuss how our results can be utilized to generate a quantitative network of links between competences across disciplines, thus acting as an enabler for identifying and bridging communities of practice, in nuclear and beyond.Description
Funding Information: Interviewees are from the consortium of PETRUS (Project for Education, Training and Research for Underground Storage), supported by the European Commission, with the objective to promote Education and Training in geological disposal of radioactive nuclear waste. Since 2005, PETRUS has coordinated universities, radioactive waste management organizations, training providers, and research institutes to develop cooperative approach to nuclear waste disposal. The PETRUS consortium proposes strategies to ensure the continuation, renewal and improvement of professional skills by sharing resources from both academia and industries, and includes 21 representatives from 12 different countries around Europe [ 20 ]. As such, PETRUS provides ample real-world knowledge and experts in the nuclear field to validate the practical relevance of our research. Furthermore, the PETRUS agenda deals with the modelling and linking of transferable skills and competences across different organizations and sectors, thus it provides good reference point for understanding the challenges and implications of linking of communities of practice in the nuclear domain. Publisher Copyright: © 2023 Korean Nuclear Society
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Kuo, V, Filz, G H & Leveinen, J 2024, 'Artificial intelligence approach for linking competences in nuclear field', Nuclear Engineering and Technology, vol. 56, no. 1, pp. 340-356. https://doi.org/10.1016/j.net.2023.10.006