Foundations of Intelligent Knowledge Management

Knowledge or hidden patterns discovered by data mining from large databases has great novelty, which is often unavailable from experts’ experience. Its unique irreplaceability and complementarity has brought new opportunities for decision-making and it has become important means of expanding knowledge bases to derive business intelligence in the Big Data era. Instead of considering how domain knowledge can play a role in each stage of data mining process, this chapter concentrates on a core problem: whether the results of data mining can be really regarded as “knowledge”. The reason is that if the domain knowledge is quantitatively presented, then the theoretical foundation can be explored for finding automatic mechanisms (algorithms) to use domain knowledge to evaluate the hidden patterns of data mining. The results will be useful or actionable knowledge for decision makers. To address this issue, the theory of knowledge management should be applied. Unfortunately, there appears little work in the cross-field between data mining and knowledge management. In data mining, researchers focus on how to explore algorithms to extract patterns that are non-trivial, implicit, previously unknown and potentially useful, but overlook the knowledge components of these patterns. In knowledge management, most scholars investigate methodologies or frameworks of using existing knowledge (either implicit or explicit ones) support business decisions while the detailed technical process of uncovering knowledge from databases is ignored.