Artificial intelligence approach for linking competences in nuclear field

Vincent Kuo*, Günther H. Filz, Jussi Leveinen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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.

Original languageEnglish
Pages (from-to)340-356
Number of pages17
JournalNuclear Engineering and Technology
Issue number1
Early online date13 Oct 2023
Publication statusPublished - Jan 2024
MoE publication typeA1 Journal article-refereed


  • Artificial intelligence
  • Community of practice
  • Competence management
  • Latent semantic analysis
  • Natural language processing
  • Nuclear knowledge management
  • Semantic technology


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