A model for assessment of uncertainty in tacit knowledge acquisition

Abstract

Purpose Expert systems have come to the forefront in the modeling of problems. One of the major problems facing the expert system designers is to develop an accurate knowledge base and a meaningful model of uncertainty associated with complex models. Decision-making is based on knowledge, and decision system support needs a knowledge base as well. An adequate knowledge acquisition (KA) process leads to accurate knowledge and improves the decision-making process. To manage the risk of a medical service (twin pregnancy in this case) a knowledge management system was created. The captured knowledge may be associated with an uncertainty. This study aims to introduce a method for evaluating the reliability of a tacit KA model. It assisted engineering managers in assessing and prioritizing risks. The study tried to use this method in risk management and new case in the health domain. Design/methodology/approach In this study, relevant variables were identified in the knowledge management literature reviews and the domain of expertise management. They are validated by a group of domain experts. Kendall’s W indicator was used to assess the degree of consensus. On the basis of combined cognitive maps, a cognitive network was constructed. Using Bayesian belief networks and fuzzy cognitive maps, an uncertainty assessment method of tacit KA was introduced. To help managers focus on major variables, a sensitivity analysis was conducted. Reliability of model was calculated for optimistic and pessimistic values. The applicability and efficacy of the proposed method were verified and validated with data from a medical university. Findings Results show that tacit KA uncertainty can be defined by independent variables, including environmental factors, personality and acquisition process factors. The reliability value shows the accuracy of the captured knowledge and the effectiveness of the acquisition process. The proposed uncertainty assessment method provides the reliability value of the acquisition model for knowledge engineers, so it can be used to implement the project and prevent failures in vital factors through necessary actions. If there is not a satisficed level of reliability, the KA project reliability can be improved by risk factors. The sensitivity analysis can help to select proper factors based on the resources. This approach mitigated some of the disadvantages of other risk evaluation methods. Originality/value The contribution of this study is to combine the uncertainty assessment with tacit KA based on fuzzy cognitive maps and the Bayesian belief networks approach. This approach used the capabilities of both narrative and computational approaches.