knowledge engineer

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.^[1]

At present, it refers to the building, maintaining and development of knowledge-based systems.^[2] It has a great deal in common with software engineering, and is used in many computer science domains such as artificial intelligence ^[3],^[4], 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. Recently, emerges meta-knowledge engineering^[5] as a new formal systemic approach to the development of a unified knowledge and intelligence theory.

Knowledge engineering principles

Since the mid-1980s, knowledge engineers have developed a number of principles, methods and tools that have considerably improved the process of knowledge acquisition and ordering. Some of the key principles are summarized as follows:^{[citation needed]}

• Knowledge engineers acknowledge that there are different types of knowledge, and that the right approach and technique should be used for the knowledge required.
• Knowledge engineers acknowledge that there are different types of experts and expertise, such that methods should be chosen appropriately.
• Knowledge engineers recognize that there are different ways of representing knowledge, which can aid the acquisition, validation and re-use of knowledge.
• Knowledge engineers recognize that there are different ways of using knowledge, so that the acquisition process can be guided by the project aims (goal-oriented).
• Knowledge engineers use structured methods to increase the efficiency of the acquisition process.
• Knowledge Engineering is the process of eliciting Knowledge for any purpose be it Expert system or AI development

Views of knowledge engineering

There are two main views to knowledge engineering:^{[citation needed]}

• Transfer View – This is the traditional view. In this view, the assumption is to apply conventional knowledge engineering techniques to transfer human knowledge into artificial intelligence systems.
• Modeling View – This is the alternative view. In this view, the knowledge engineer attempts to model the knowledge and problem solving techniques of the domain expert into the artificial intelligence system.

Some methodologies that support the development of knowledge or intelligence-based systems include:

• CommonKADS

A major concern in knowledge engineering is the construction of ontologies. One philosophical question in this area is the debate between foundationalism and coherentism - are fundamental axioms of belief required, or merely consistency of beliefs which may have no lower-level beliefs to justify them?

Bibliography

1. ^ Feigenbaum, Edward A.; McCorduck, Pamela (1983), The fifth generation (1st ed.), Reading, MA: Addison-Wesley, ISBN 9780201115192, OCLC 9324691 
2. ^ Kendal, S.L.; Creen, M. (2007), An introduction to knowledge engineering, London: Springer, ISBN 9781846284755, OCLC 70987401 
3. ^ Negnevitsky, Michael (2005). Artificial Intelligence: A Guide to Intelligent Systems. Addison Wesley. ISBN 0-321-20466-2. 
4. ^ Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, NJ: Prentice Hall, ISBN 0-13-790395-2, http://aima.cs.berkeley.edu/ 
5. ^ http://hid.casaccia.enea.it/keywords-a.htm

• Penrose, Roger (2005). The Emperor's New Mind: Concerning Computers, Minds and the Laws of Physics. Owl Books (NY). ISBN 0-8050-7853-3. OCLC 61273290. 

See also

External links

• Data & Knowledge Engineering - Elsevier Journal
• Knowledge Engineering Review, Cambridge Journal
• The International Journal of Software Engineering and Knowledge Engineering- World Scientific
• IEEE Transactions on Knowledge and Data Engineering
• Expert Systems: The Journal of Knowledge Engineering - Wiley-Blackwell