20. Intelligent Knowledge Management

In deriving knowledge by technical means, data mining becomes popular for the process of extracting knowledge, which is previously unknown to humans, but potentially useful from a large amount of incomplete, noisy, fuzzy and random data. Knowledge discovered from algorithms of data mining from large-scale databases has great novelty, which is often beyond the experience of experts. Its unique irreplaceability and complementarity has brought new opportunities for decision-making. Access to knowledge through data mining has been of great concern for business applications, such as business intelligence. However, from the perspective of knowledge management, knowledge discovery by data mining from large-scale databases face several challenging problems, therefore we call the knowledge or hidden patterns discovered from data mining the “rough knowledge”. Such knowledge has to be examined at a “second order” in order to derive the knowledge accepted by users or organizations. In this chapter, we defined the new knowledge “intelligent knowledge”, proposed the framework of the management process of intelligent knowledge (intelligent knowledge management, IKM), and other theoretical results.