This topic is a modern discipline part of artificial intelligence aiming to design and develop expert sytems (or knowledge-based systems). It is supported by instructional methodologies, computer science and information technologies, attempting to represent human knowledge and human reasoning for particular domains inside of an artificial system.

Since a knowledge engineer is not an expert in the particular field to be modeled, as well as, the domain expert has not experience modeling his knowledge in a general computational system, the job of knowledge engineers is to extract human experts knowledge for a given area and codify such knowledge so that it can be be automatically processed by a computational system.

Knowledge engineering brings together scientists, technology and methodology in order to process human knowledge. The aims of this research topic is to extract, articulate and automate knowledge of a human expert. It is close related with mathematical logic, considering aspects of cognitive science and socio-cognitive engineering, where the knowledge is produced by socio-cognitive aggregates (mainly human beings) and structured according to our own knowledge about how reasoning and logic works in humanity. A recent area of research is meta-cognitive engineering, which evolves from a new formal systemic approach  for the development of a unified knowledge and intelligence theory.

Some particular case studies for knowledge engineering are the following ones:

• Problem evaluation
• Knowledge-base system development, maintenance, revision and evaluation
• Information acquisition, information and knowledge structure and preferences
• Implementation of structured knowledge in knowledge databases
• Validation and verification of knowledge stored in databases
• Knowledge representation

The research group Language Knowledge Engineering (LKE) is structured according to the interests of members. The following table indicates a summary of such interests: