The aim of our paper is to explain a mathematical model as a special case of symbolic knowledge map. Each knowledge mapping is a visualization of knowledge for the purpose of eliciting, sharing and expanding. Tools of such visualization can be of various types. But in reality many types of so‑called knowledge maps are only data flow or information flow diagrams. Our paper will define the most important features which every knowledge map must satisfy, for instance it must include chronological, hierarchical, associative, causal and evaluative relationships, it must improve the quality of knowledge etc. In our paper we will prove that a mathematical model satisfies all requirements to be called a knowledge map. Neither definition nor categorization and taxonomy of knowledge mapping are unified in the literature so the authors try to start with working on this field. Knowledge map is a visual interception of knowledge with the aim of its storage, sharing and development. Weak descriptive knowledge maps may be used for explaining the ideas and concepts connected with OR models, as well as for explaining the new knowledge gained with the models, in a well‑ structured form. Strong descriptive knowledge maps can serve to describe real relations between the objects of the models or real elements in relation to their positioning. In this case the object placing does not describe only its physical position but also, for instance, its economical indexes. Like the normative OR models, the normative knowledge maps show the normative solution, or help to find the best, desirable or advisable solution. After suggestions of how to categorize knowledge maps (above) mathematical models of various types with all features and properties are presented as a knowledge map.
Keywords: knowledge map, knowledge map categorization, mathematical model, model construction, algorithm, model solution
Knowledge Management has become the most strategic resource in the new business environment. This research is based on the analysis of the strategic knowledge held within a multinational group; a leader in the design and production of a great variety of components for the automotive industry. It focuses on achieving feasibility and real applications by identifying knowledge gaps that must be overcome to perform certain activities, so as to take the right decision on its acquisition in terms of what to acquire, how to acquire it, and the associated time and costs. We use a recently developed artificial neural architecture called Cooperative Maximum‑Likelihood Hebbian Learning, a tool to develop part of an Integral Global Model of Business Management, which has the potential to bring about a global improvement in the firm by adding value, flexibility and competitiveness. From this perspective, the model used in the study generalizes the hypothesis of organizational survival and competitiveness, so that the organization is able to identify, strengthen, and use key knowledge to reach pole position. Our conclusions suggest that it is possible to specify the knowledge that is held but is underused in the departments, taking into account their current levels of knowledge, their relevance and the urgency to acquire new knowledge. Moreover, an analysis of the required evolution rate of the present knowledge may be included which, among other aspects helps detect new knowledge, eliminate obsolete knowledge and validate new needs.