Implementation of Decision Tree Algorithm to Classify Knowledge Quality in a Knowledge Intensive System

¹ School of Computing and IT, Taylor’s University, Subang Jaya, Selangor, Malaysia
² School of Computing and IT, Taylor’s University, Subang Jaya, Selangor, Malaysia
³ School of Computing and IT, Taylor’s University, Subang Jaya, Selangor, Malaysia
⁴ Agile Management Consultancy, Malaysia

^* Corresponding author: caspergihessimon@sd.taylors.edu.my

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.