Proposing a model for dynamical computing the risk of knowledge domains in the organizational knowledge map

Abstract

Purpose This paper aims to present a specific model and method for analyzing the knowledge domains and organization knowledge map. One of the functions of the organizational knowledge map is the possibility of extracting the risk of the organization's knowledge domains and thus the ability to define knowledge-based strategies in the organization. So far, various software tools have been designed to support the process of creating, analyzing knowledge domains and structure knowledge map. However, software companies have less detail and methodology of their software analysis and less research has been addressed. Design/methodology/approach This model calculates the risk of knowledge domains using the recursive algorithm approach, assuming there is one-way communication between the knowledge domains and using specific factors. Findings The prominence of this model is to calculate knowledge domains risk, dynamic updating of knowledge domains risk after any changes in knowledge domain risk in the organization's knowledge map. The model can also be used as a simulation model and prioritize corrective actions. Originality/value This is a recursive model that by assuming one-way relations among knowledge domain computes the risk of each domain knowledge by considering the risk of its related domains, relations among different domains and pre-requisites and post-requisites. This model has no limitation in determining the number of knots and communications. Despite of simplicity, it is too efficient and any organization can localize it based on its own needs.

Research on the Era Value and Communication Path of Human Destiny Community and Socialist Core Values in the Context of Big Data

“…In this section, the existing propagation model is improved by introducing the association structure, and the overall framework of the node influence propagation model is shown in Figure 2. The node influence propagation model based on the knowledge graph first needs to portray the influence of nodes, that is, which nodes have a more important influence on the overall network [28][29][30]. After analyzing the important nodes, we divide the overall network into associations and then improve the modeling analysis of the propagation model in macro and micro perspectives, respectively.…”