Back to Search
Start Over
Optimisation of Knowledge Management (KM) with Machine Learning (ML) Enabled.
- Source :
- Information (2078-2489); Jan2023, Vol. 14 Issue 1, p35, 15p
- Publication Year :
- 2023
-
Abstract
- The emergence of artificial intelligence (AI) and its derivative technologies, such as machine learning (ML) and deep learning (DL), heralds a new era of knowledge management (KM) presentation and discovery. KM necessitates ML for improved organisational experiences, particularly in making knowledge management more discoverable and shareable. Machine learning (ML) is a type of artificial intelligence (AI) that requires new tools and techniques to acquire, store, and analyse data and is used to improve decision-making and to make more accurate predictions of future outcomes. ML demands big data be used to develop a method of data analysis that automates the construction of analytical models for the purpose of improving the organisational knowledge. Knowledge, as an organisation's most valuable asset, must be managed in automation to support decision-making, which can only be accomplished by activating ML in knowledge management systems (KMS). The main objective of this study is to investigate the extent to which machine learning applications are used in knowledge management applications. This is very important because ML with AI capabilities will become the future of managing knowledge for business survival. This research used a literature review and theme analysis of recent studies to acquire its data. The results of this research provide an overview of the relationship between big data, machine learning, and knowledge management. This research also shows that only 10% of the research that has been published is about machine learning and knowledge management in business and management applications. Therefore, this study gives an overview of the knowledge gap in investigating how ML can be used in KM for business applications in organisations. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20782489
- Volume :
- 14
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Information (2078-2489)
- Publication Type :
- Academic Journal
- Accession number :
- 161480689
- Full Text :
- https://doi.org/10.3390/info14010035