Knowledge modelling

What is knowledge modelling?

Knowledge modelling deals with the Representation of knowledge in a form that can be interpreted by both humans and machines and is used in knowledge-based systems. It is a sub-area of knowledge management and the Artificial intelligence.

In order to be able to represent knowledge, several steps are required. The first is knowledge acquisition, which is divided into explicit knowledge, clearly represented by language and signs, and tacit knowledge, which becomes visible through an action. In the second step, knowledge representation, the acquired knowledge must be formalised. This is usually done by building a knowledge database or new links in the brain. In a third step, approaches to solving the problems are found before the knowledge is represented in the fourth step.

What are practical examples of knowledge modelling?

The Knowledge modelling is mainly applied in the manufacturing industrywhich is already heavily based on machine learning build. This model is used in the aviation and automotive industries. Both branches rely on the processing and communication of information for the continuous optimisation of processes and products. This includes, among other things, the Design, manufacturing and innovation process.

With the help of knowledge-based engineering, new products are manufactured quickly and efficiently and brought to market. In doing so, the companies can draw on existing knowledge from production. Of course, this requires very good knowledge management and precise knowledge modelling.

However, much of this knowledge is tacit, i.e. it is not available in a formalised form and is therefore difficult to retrieve and update. It is often also called tacit or personal knowledge. This is where knowledge modelling comes in. It is used to select types of knowledge that are needed for specific processes.
For this purpose, knowledge is summarised in a structured way by means of various procedures and methods and a formalised representation is made possible. This creates knowledge that can be processed and retrieved by computers and machines, i.e. the knowledge base. This then allows efficient implementation of new processes and workflows.

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