From information- to knowledge-management: the role of rule induction and neural net machine learning techniques in knowledge generation

Abstract

Developments in artificial intelligence mean that it is now increasingly possible to store not only information but also knowledge as an exploitable resource. Insofar as he or she is concerned with creating, organizing and monitoring knowledge resources to support effective decision making within an organization, the information manager is developing the role of knowledge manager. As well as its organization and dissemina tion, the generation of storable knowledge is very much on the agenda of the knowledge manager. The extent to which com puters can help in the process of knowledge generation is central to his or her concerns. Machine learning techniques have been developed which are capable of giving us an increasing amount of help in this process. The contributions of rule induction and artificial neural net systems are discussed. It is likely that such tech niques will prove to be useful tools both for the information/knowledge manager requiring practical working systems enabling the cost-effective exploitation of knowledge resources, and for the information/knowledge scientist requir ing advances in our more fundamental theoretical knowledge of the nature of information and ways of processing it.