Knowledge engineering (KE) was defined in 1983 by Edward Feigenbaum, and Pamela McCorduck as follows:KE is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise.
At present, it refers to the building, maintaining and development of knowledge-based systems. It has a great deal in common with software engineering, and is used in many computer science domains such as artificial intelligence, including databases, data mining, expert systems, decision support systems and geographic information systems. Knowledge engineering is also related to mathematical logic, as well as strongly involved in cognitive science and socio-cognitive engineering where the knowledge is produced by socio-cognitive aggregates (mainly humans) and is structured according to our understanding of how human reasoning and logic works.
Various activities of KE specific for the development of a knowledge-based system:
- Assessment of the problem
- Development of a knowledge-based system shell/structure
- Acquisition and structuring of the related information, knowledge and specific preferences (IPK model)
- Implementation of the structured knowledge into knowledge bases
- Testing and validation of the inserted knowledge
- Integration and maintenance of the system
- Revision and evaluation of the system.
Being still more art than engineering, KE is not as neat as the above list in practice. The phases overlap, the process might be iterative, and many challenges could appear.
Other articles related to "knowledge engineering, knowledge":
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... The primary aim of knowledge engineering is to attain a productive interaction between the available knowledge base and problem solving techniques ... Thus, the first essential component of knowledge engineering is a large “knowledge base.” Dendral has specific knowledge about the mass spectrometry ... This “knowledge base” is used both to search for possible chemical structures that match the input data, and to learn new “general rules” that help prune searches ...
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