| Knowledge Management Systems (KMS) have become so imperative in organizations today because they are efficient in knowledge capture, storage, and dissemination. This paper reviews different types and technologies of KMS, placing special emphasis on AI-based systems, such as Knowledge-Based Expert Systems (KEBS), Neural Networks (NN), and Case-Based Reasoning (CBR). This work will also discuss, to an extent, KMS adopted by governmental organizations, process-oriented KMS, and contingency KMS by describing the benefits and challenges that come with each. Further, this paper will increase the possibilities of knowledge representation and knowledge retrieval by discussing Knowledge Graph Technology. In addition, it discusses issues that arise during implementation; issues such as poor quality data and how to balance human expertise with AI-driven automation. Thereafter, it will shed some light on future trends and recommendations about how to have optimal adoption of KMS in different types of industries. |
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