Knowledge engineering is one of the building blocks of artificial intelligence (AI).
It attempts to emulate the judgment and behavior of a human with
expertise in a field or domain. In this lesson, we’ll define knowledge
engineering and its processes, understand where it fits within the AI
landscape, and provide real-world examples of its application.
Imagine
an education company wanting to automate the teaching of children in
subjects from biology to computer science (requiring to capture the
knowledge of teachers and subject matter experts) or Oncologists
choosing the best treatment for their patients (requiring expertise and
knowledge from information contained in medical journals, textbooks, and
drug databases).
Knowledge
Engineering is the process of imitating how a human expert in a
specific domain would act and take decisions. It looks at the metadata
(information about a data object that describes characteristics such as
content, quality, and format), structure and processes that are the
basis of how a decision is made or conclusion reached. Knowledge
engineering attempts to take on challenges and solve problems that would
usually require a high level of human expertise to solve.
Figure 1
illustrates the knowledge engineering pipeline.

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