engineering

Knowledge engineering

Knowledge engineering is a engineering topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Knowledge engineering rather than just read about it. In short: Knowledge engineering (KE) refers to all aspects involved in knowledge-based systems. Background Expert systems One of the first examples of an expert system was MYCIN, an application to perform medical diagnosis.

Key takeaways

Knowledge engineering belongs to engineering; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Knowledge engineering to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Knowledge engineering from memory before moving on to harder problems.

Reference excerpt

Knowledge engineering (KE) refers to all aspects involved in knowledge-based systems.

Background

Expert systems One of the first examples of an expert system was MYCIN, an application to perform medical diagnosis. In the MYCIN example, the domain experts were medical doctors and the knowledge represented was their expertise in diagnosis. Expert systems were first developed in artificial intelligence laboratories as an attempt to understand complex human decision making. Based on positive results from these initial prototypes, the technology was adopted by the US business community (and later worldwide) in the 1980s. The Stanford heuristic programming project led by Edward Feigenbaum was one of the leaders in defining and developing the first expert systems.

History In the earliest days of expert systems, there was little or no formal process for the creation of the software. Researchers just sat down with domain experts and started programming, often developing the required tools (e.g. inference engines) at the same time as the applications themselves. As expert systems moved from academic prototypes to deployed business systems it was realized that a methodology was required to bring predictability and control to the process of building the software. There were essentially two approaches that were attempted:

Use conventional software development methodologies Develop special methodologies tuned to the requirements of building expert systems Many of the early expert systems were developed by large consulting and system integration firms such as Andersen Consulting. These firms already had well tested conventional waterfall methodologies (e.g. Method/1 for Andersen) that they trained all their staff in and that were virtually always used to develop software for their clients. One trend in early expert systems development was to simply apply these waterfall methods to expert systems development. Another issue with using conventional methods to develop expert systems was that due to the unprecedented nature of expert systems, they were one of the first applications to adopt rapid application development methods that feature iteration and prototyping as well as or instead of detailed analysis and design. In the 1980s few conventional software methods supported this type of approach. The final issue with using conventional methods to develop expert systems was the need for knowledge acquisition. Knowledge acquisition refers to the process of gathering expert knowledge and capturing it in the form of rules and ontologies. Knowledge acquisition has special requirements beyond the conventional specification process used to capture most business requirements. These issues led to the second approach to knowledge engineering: the development of custom methodologies specifically designed to build expert systems. One of the first and most popular of such methodologies custom designed for expert systems was the Knowledge Acquisition and Documentation Structuring (KADS) methodology developed in Europe. KADS had great success in Europe and was also used in the United States.

See also Knowledge level modeling Knowledge management Knowledge representation and reasoning Knowledge retrieval Knowledge tagging Method engineering

References

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 Archived 2009-05-15 at the Wayback Machine Expert Systems: The Journal of Knowledge Engineering – Wiley-Blackwell

Worked examples

Example 1 — a first encounter with Knowledge engineering

Start with the simplest possible case. Write down what Knowledge engineering claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In engineering, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Knowledge engineering before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Knowledge engineering ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Knowledge engineering

In research: Knowledge engineering appears in engineering research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Knowledge engineering in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Knowledge engineering is common in secondary-school and first-year university syllabi. It links to neighbouring topics Knowledge engineering, Ontology (information science), Semantic Web, so understanding it makes those chapters shorter.
In everyday life: Look for Knowledge engineering outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Knowledge engineering in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Knowledge engineering means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Knowledge engineering out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Knowledge engineering in simple terms?

Knowledge engineering (KE) refers to all aspects involved in knowledge-based systems. Background Expert systems One of the first examples of an expert system was MYCIN, an application to perform medical diagnosis.

Why does Knowledge engineering matter?

Because it connects several engineering ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Knowledge engineering?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Knowledge engineering.