The role of classi cation(s) in distributed knowledge management

Most knowledge management (KM) projects aim at creating a knowledge base system in which all corporate knowledge is organized according to a single, supposedly shared and objective classi cation. The underlying assumption is that knowledge can be made objective re ning it of all its subjective, contextual, and social aspects. However, a lot of work in disciplines like arti cial intelligence, cognitive science, philosophy, linguistics, show that such an objectivistic epistemology is incompatible with the very nature of knowledge, and it is, therefore, one reason why KM systems are often deserted by users. Another approach, called Distributed Knowledge Management (DKM), is proposed in which subjective and social aspect are seriously token into account. Using this approach, we discuss a high level technological architecture, in which we introduce the idea of local classi cation, namely a classi cation created and maintained by a single organizational unit (e.g., a community or a division), which we call knowledge nodes (KNs). A system for DKM becomes a tool that supports two qualitatively different processes: the autonomous management of local classi cations within each knowledge node (principle of autonomy), and the coordination of the different KNs via a process of an agent-mediated meaning negotiation/coordination across different classi cations (principle of coordination).