A finite base revision approach originally proposed in Dargam (1996). The compromises adopted are of two types: (1) If the input sentence cannot be added to the knowledge base K, we compromise by allowing its consistent consequences to be added to K . This case is referred to as “the input compromise.” (2) The input can be added to the base, K is revised and we compromise by allowing the consistent consequences of the retracted sentences to be in the revised base. This case is referred to as “the retracted sentences compromise.” By adopting these compromises, less loss of information from the requested update of the knowledge base is achieved.
Published in Chapter:
Finite-Base Revision Supporting Knowledge Management and Decision Making
Fátima C.C. Dargam (SimTech Simulation Technology – Graz, Austria and ILTC, Instituto Doris Aragon – Rio de Janeiro, Brazil)
Copyright: © 2008
|Pages: 8
DOI: 10.4018/978-1-59904-843-7.ch043
Abstract
Generation and most of all sustainability of organizational success rely heavily on proper decision making and on the application of knowledge management (KM) concepts, where knowledge-based structures are fundamental components. KM can also be viewed as a means to support enhanced decision making through effective control of organizational knowledge. One of the main goals of KM is to capture, codify, organize, and store relevant knowledge into repositories, knowledge bases (KB), for later retrieval and use by organizations. However, there is always the danger of accumulating knowledge in an increasingly vast way, such that it becomes impossible to process it when necessary. Therefore, appropriate technologies have to be identified to protect us from irrelevant information. As the study in Handzic (2004) shows, decision-makers need to pursue primarily one KM strategy in order to use knowledge effectively. Moreover, the codification KM strategy using procedural knowledge maps was proven to be quite appropriate for solving decision problems of a complex nature. It is commonly agreed that KM can bridge the existing information and communication gaps within organizations, consequently improving decision making (Dargam & Rollett, 2007).