Abstract
Knowledge is an important asset for an organisation as it facilitates organisational growth. To facilitate knowledge creation and sharing, this is where a knowledge-intensive system is required. One key area that hinders the effective use of knowledge-intensive systems in an organisation is the lack of knowledge quality. This causes the system to be underutilised, and as a result, knowledge will not be captured or shared effectively. Recent KM findings identified that machine learning could be beneficial to knowledge management. A literature review was conducted to identify knowledge of quality attributes and machine learning algorithms. From the findings, it was identified that the decision tree algorithm has a strong potential at classifying knowledge quality. An experiment was then devised to identify the training model required and measure its effectiveness using a pilot test. This involved using a knowledge-intensive system and mapping its variables to the respective knowledge quality attributes. From the experimentation result, the training model is then devised before implemented in a pilot test. The pilot test involved collecting knowledge using the same knowledge-intensive system before running the training model. From the results, it was identified that the decision tree could classify knowledge quality though the results yielded four different outputs at classifying knowledge quality. It was concluded that machine learning is beneficial in the area of knowledge management.
Highlights
Knowledge management is becoming relevant and important for an organisation to survive or thrive in the world today
From a machine learning perspective, we can see how the model reacts with the pilot test data with its minimal class separation and predictor connection
From a knowledge management perspective, we can clearly see the benefit of applying machine learning
Summary
Knowledge management is becoming relevant and important for an organisation to survive or thrive in the world today. The usage of knowledge-intensive systems is affected by the quality of knowledge present in the system [13,14,15]. One area of knowledge management could benefit from is machine learning. It was identified that machine learning could be beneficial to knowledge management, and its benefits must be looked at [16, 17]. Machine learning algorithms can be used to further enhance a knowledge-intensive system [18]. This is because machine learning is applied in a multitude of tasks [19,20,21,22,23]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
