Knowledge Engineering in AI - Definition, Process

What is Knowledge Engineering?

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. Below Image illustrates the knowledge engineering pipeline.

Knowledge Engineering Processes

In terms of its role in artificial intelligence (AI), knowledge engineering is the process of understanding and then representing human knowledge in data structures, semantic models (conceptual diagram of the data as it relates to the real world) and heuristics (rules that lead to solution to every problem taken in AI).

Expert systems, and algorithms are examples that form the basis of the representation and application of this knowledge.
The knowledge engineering process includes:

• Knowledge acquisition
• Knowledge representation
• Knowledge validation
• Inferencing
• Explanation and justification

The interaction between these stages and sources of knowledge is shown in Figure below.
 

The amount of collateral knowledge can be very large depending on the task. A number of advances in technology and technology standards have assisted in integrating data and making it accessible.

These include the semantic web (an extension of the current web in which information is given a well-defined meaning), cloud computing (enables access to large amounts of computational resources), and open datasets (freely available datasets for anyone to use and republish). These advances are crucial to knowledge engineering as they expedite data integration and evaluation.

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