Data, Information, Knowledge, Wisdom: A Revised Model for Agents-Based Knowledge Management Systems

Abstract

Learning in distributed learning (DL) environment sometimes involves knowledge management (KM) in particular at doctoral level where students can create and share collective knowledge while developing research skills. A key concept in KM is the so-called knowledge pyramid (KP), which describes the hierarchy of data, information, knowledge, and wisdom (DIKW). Recognizing differences between these concepts is important in the KM literature. However, less attention has been given to the transformation processes between data, information, knowledge, and wisdom, and no knowledge management system (KMS) is explicitly based on the KP and its processes. Fundamentally, data is a set of discrete facts about events and the world, information is structured data with meaning, knowledge is interpreted information based on an agent’s belief and put into a context, and wisdom is an agent’s ability to exploit the knowledge to behave efficiently based on rules in a specific context to reach its goal. Therefore, there are intelligent processes that convert data into information, information into knowledge, and knowledge into wisdom. This paper identifies the types of processes involved and the kinds of intelligence an intelligent agent can deploy to transform data into information, information into knowledge, and knowledge into wisdom in a KMS. Then it presents a model of the KP suitable for agents-based KMSs. These findings are shown to lead to a different approach in designing and developing agents-based KMSs to support learners in DL.