knowledge engineering
In plain wordsThe craft of collecting what human experts know — through interviews, observation and documents — and turning it into rules or structured data that a computer system can use.
The discipline of building knowledge-based systems: eliciting knowledge from domain experts and documents, structuring it, encoding it in a knowledge representationknowledge representation, n.: The study of how to record knowledge about objects, categories, rules and events in a precise format, so that a program can reason with it and reach conclusions nobody typed in. formalism such as rules, framesframe, n.: In classic AI, a ready-made template for a familiar kind of thing or event, such as a kitchen or a job interview, with blanks to fill in and sensible default values. or ontologiesontology, n.: A carefully defined vocabulary for a subject such as medicine, spelling out each kind of thing, how kinds nest inside one another, and which connections between them are allowed., and validating and maintaining the resulting knowledge baseknowledge base, n.: A store of facts and rules that a program can reason with: like a database, but organized so that new conclusions can be worked out from it, not just looked up.. A knowledge engineer mediates between experts and the system, using techniques such as structured interviews, think-aloud protocol analysis, card sorting and repertory grids; later methodologies such as CommonKADS treated the work as building models of expertise rather than transferring rules. The difficulty and cost of this process became known as the knowledge acquisition bottleneckknowledge acquisition bottleneck, n.: The problem that getting knowledge out of human experts and into a computer by hand is so slow and costly that it limited how far classic expert systems could go.. The skills survive in ontology engineering, enterprise knowledge graphknowledge graph, n.: A huge web of facts stored as connections between things — 'Paris is the capital of France', 'Marie Curie won a Nobel Prize' — that computers can search, combine and reason over. construction, and the writing of specifications, rubrics and evaluation criteria for language-modellarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code. systems.
Origin
Donald Michie of Edinburgh used the phrase as the title of a 1973 paper in Kybernetes. Edward Feigenbaum made it the name of a discipline in his 1977 IJCAI paper 'The Art of Artificial Intelligence: Themes and Case Studies of Knowledge Engineering', drawing on Stanford's Heuristic Programming Project (DENDRALDENDRAL, prop. n.: A program begun at Stanford in 1965 that worked out the structure of chemical molecules from laboratory measurements using rules taken from expert chemists. It is usually called the first expert system., MYCINMYCIN, prop. n.: A 1970s Stanford program that advised doctors on blood infections and antibiotics by applying hundreds of if–then rules from specialists. It performed about as well as experts but was never used on patients.), and his book with Pamela McCorduck, The Fifth Generation (1983), carried the term to a general audience.
Related
- expert systemexpert system, n.: A program that stores a human specialist's know-how as many if–then rules and uses them to give advice or make decisions in a narrow field, such as diagnosing infections or configuring computers.
- knowledge baseknowledge base, n.: A store of facts and rules that a program can reason with: like a database, but organized so that new conclusions can be worked out from it, not just looked up.
- knowledge acquisition bottleneckknowledge acquisition bottleneck, n.: The problem that getting knowledge out of human experts and into a computer by hand is so slow and costly that it limited how far classic expert systems could go.
- knowledge representationknowledge representation, n.: The study of how to record knowledge about objects, categories, rules and events in a precise format, so that a program can reason with it and reach conclusions nobody typed in.
- ontologyontology, n.: A carefully defined vocabulary for a subject such as medicine, spelling out each kind of thing, how kinds nest inside one another, and which connections between them are allowed.
- Edward FeigenbaumEdward Feigenbaum, prop. n.: An American computer scientist who pioneered expert systems, programs that capture specialists' knowledge as rules to solve problems in fields such as chemistry and medicine.
Sources
- Michie, D. (1973). Knowledge Engineering. Kybernetes 2(4), 197–200. emerald.com/insight/content/doi/10.1108/eb005338/full/html
- Feigenbaum, E. A. (1977). The Art of Artificial Intelligence: Themes and Case Studies of Knowledge Engineering. Proceedings of IJCAI-77, 1014–1029.
- Schreiber, G. et al. (2000). Knowledge Engineering and Management: The CommonKADS Methodology. MIT Press.
Mentioned in
CycCyc, prop. n.: An AI system built by specialists who spent decades typing in millions of everyday truths, such as 'people cannot be in two places at once', hoping to give computers common sense., knowledge baseknowledge base, n.: A store of facts and rules that a program can reason with: like a database, but organized so that new conclusions can be worked out from it, not just looked up., knowledge acquisition bottleneckknowledge acquisition bottleneck, n.: The problem that getting knowledge out of human experts and into a computer by hand is so slow and costly that it limited how far classic expert systems could go.