Situation Analysis for Knowledge-Based Systems

The term knowledge-based systems, generally refers to expert system approaches to prediction or decision support. In reality, knowledge-based systems include all predictive and decision support models. The growing recognition of the need to integrate, manage, and share knowledge has led to the emergence of the new " magic bullet " called knowledge management. The " new " knowledge management paradigm proposes to capture informal knowledge based on personal experience and learning, and to integrate it with more discipline or domain specific formal knowledge in a knowledge base. The current situation The term knowledge management has become popular and we are seeing frequent press references to knowledge management and its importance to the survival of companies in the next millennium. In reality, it is not new. It is, rather, newly popular or perhaps newly understood outside of academic circles. It builds on the successes and failures of data and information management activities and applications. Knowledge management is part of the continuum of data ! information ! knowledge ! wisdom. Knowledge management research is expanding the tools available for knowledge capture beyond traditional empirical, process, and expert systems approaches. Knowledge management, like data and information management, consists of the following: creating, updating, retrieving, deleting, and archiving 1. Knowledge management should allow simple, yet powerful access to knowledge for update and use (via reasoning). It should take into account differences among users and among requirements for different problem-solving tasks. The key to effective knowledge management is the representational model used to store and access knowledge resident in the knowledge base. The field of knowledge management is highly volatile and its similarity to data management activities during the 1970's and 80's is both striking and understandable. Similar to the way many organizations were storing data in the 1970's, many organizations are already practicing elements of what is now known as knowledge management but they are doing so in an uncoordinated and frequently ad hoc manner. There is a growing recognition of the need for and value of knowledge sharing and reuse between and within systems and organizations. However, just as the major impediments to data integration were inconsistent or incompatible concepts, definitions, terminology, structures, and formats, the major impediments to knowledge integration will be inconsistent or incompatible concepts, definitions, terminology, structures, and formats. Unfortunately, the standards and protocols to enable such sharing and reuse are only just emerging. Conclusions The issue of knowledge management …