Artificial intelligence approach for linking competences in nuclear field
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 d...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Nuclear Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573323004539 |
| _version_ | 1797355276292063232 |
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| author | Vincent Kuo Günther H. Filz Jussi Leveinen |
| author_facet | Vincent Kuo Günther H. Filz Jussi Leveinen |
| author_sort | Vincent Kuo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T14:08:50Z |
| format | Article |
| id | doaj.art-3e8176c100d047df88d473531bdb6352 |
| institution | Directory Open Access Journal |
| issn | 1738-5733 |
| language | English |
| last_indexed | 2024-03-08T14:08:50Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj.art-3e8176c100d047df88d473531bdb63522024-01-15T04:20:57ZengElsevierNuclear Engineering and Technology1738-57332024-01-01561340356Artificial intelligence approach for linking competences in nuclear fieldVincent Kuo0Günther H. Filz1Jussi Leveinen2Department of Civil Engineering, Aalto University, Finland; Corresponding author. Aalto University, Department of Civil Engineering, Rakentajanaukio 4, 02150, Espoo, Finland.Department of Architecture, Aalto University, FinlandDepartment of Civil Engineering, Aalto University, FinlandBridging 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.http://www.sciencedirect.com/science/article/pii/S1738573323004539Nuclear knowledge managementCompetence managementLatent semantic analysisCommunity of practiceNatural language processingArtificial intelligence |
| spellingShingle | Vincent Kuo Günther H. Filz Jussi Leveinen Artificial intelligence approach for linking competences in nuclear field Nuclear Engineering and Technology Nuclear knowledge management Competence management Latent semantic analysis Community of practice Natural language processing Artificial intelligence |
| title | Artificial intelligence approach for linking competences in nuclear field |
| title_full | Artificial intelligence approach for linking competences in nuclear field |
| title_fullStr | Artificial intelligence approach for linking competences in nuclear field |
| title_full_unstemmed | Artificial intelligence approach for linking competences in nuclear field |
| title_short | Artificial intelligence approach for linking competences in nuclear field |
| title_sort | artificial intelligence approach for linking competences in nuclear field |
| topic | Nuclear knowledge management Competence management Latent semantic analysis Community of practice Natural language processing Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S1738573323004539 |
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