Leveraging Computational Algorithms for Effective Explicit and Tacit Knowledge Capture: A Hybrid Approach Combining Expert Interviews, Machine Learning, and Data Mining Techniques
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| Titel: | Leveraging Computational Algorithms for Effective Explicit and Tacit Knowledge Capture: A Hybrid Approach Combining Expert Interviews, Machine Learning, and Data Mining Techniques |
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| Autoren: | Zuowei Li |
| Quelle: | Applied and Computational Engineering. 114:40-45 |
| Verlagsinformationen: | EWA Publishing, 2024. |
| Publikationsjahr: | 2024 |
| Beschreibung: | This research explores a hybrid method for knowledge acquisition that combine expert interviews by human experts with machine learning and data mining to augment the accuracy and richness of explicit and implicit knowledge-creation. With organisations beginning to realize the importance of efficient knowledge management, the boundaries of the traditional methodologies to deal with high dimensional data and advanced knowledge emerge. The work suggests a hybrid approach in which expert knowledge adds context, while computational algorithms add reliability and scalability. Data reveal 15% greater accuracy and 20% greater readability than single methods, which is a testament to the strengths of hybrid techniques. The paper illustrates practical implications for decision-making, employee training, and regulatory compliance, and illustrates how hybrid strategies enable more adaptive and integrated knowledge management practices. This study advances research across academic and industrial boundaries with an effective and scalable knowledge capture model that can be extended across industry verticals. |
| Publikationsart: | Article |
| ISSN: | 2755-273X 2755-2721 |
| DOI: | 10.54254/2755-2721/2024.18200 |
| Dokumentencode: | edsair.doi...........94a3b35292570a6718aa1f9b64ca8f41 |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | This research explores a hybrid method for knowledge acquisition that combine expert interviews by human experts with machine learning and data mining to augment the accuracy and richness of explicit and implicit knowledge-creation. With organisations beginning to realize the importance of efficient knowledge management, the boundaries of the traditional methodologies to deal with high dimensional data and advanced knowledge emerge. The work suggests a hybrid approach in which expert knowledge adds context, while computational algorithms add reliability and scalability. Data reveal 15% greater accuracy and 20% greater readability than single methods, which is a testament to the strengths of hybrid techniques. The paper illustrates practical implications for decision-making, employee training, and regulatory compliance, and illustrates how hybrid strategies enable more adaptive and integrated knowledge management practices. This study advances research across academic and industrial boundaries with an effective and scalable knowledge capture model that can be extended across industry verticals. |
|---|---|
| ISSN: | 2755273X 27552721 |
| DOI: | 10.54254/2755-2721/2024.18200 |