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What is Knowledge Engineering?

What is Knowledge Engineering?

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Can artificial intelligence think and act like a person? The field of knowledge engineering is working to achieve that goal. This type of artificial intelligence engineer works to replicate the human decision-making process to solve complex problems.

Knowledge engineering is not a new field, but it is one that has undergone significant changes since its original inception. Originally utilized for medical diagnosis and satellite control, today it has become an important part of a multitude of industries, which means new and exciting opportunities for AI engineers and systems and control engineers.

Knowledge engineers work to translate human expertise into what’s called a knowledge-based system that can replicate someone’s answer. These systems tackle complex, high-level problems where an industry expert would be called upon. Because it requires large amounts of information, knowledge engineering has also changed due to new technology like cloud storage systems. For example, an oncologist would search journals, textbooks and drug databases to find the right treatment for their patient, which would require a great deal of data for a program store.1 In addition to this knowledge itself, that system would require rules to decide how that medical information will be applied in different circumstances.

Knowledge engineering has been called a part of the fourth industrial revolution. It’s already being used in industries such as healthcare, customer service, financial services, manufacturing and law.2 These AI engineers are also responsible for technologies such as facial recognition and programs that understand human speech as well as product development like Amazon’s Alexa.3

Why is knowledge engineering important?

Knowledge engineering can increase the speed of decision-making for an organization. More importantly, it has the potential to develop better solutions to more challenging problems.

Companies need to be able to handle larger and larger amounts of information at faster speeds. They can use machine learning and algorithms to help identify ways to improve productivity and quality, but these systems eventually need humans to take over the decision-making process.

These are the problems that need an expert to solve. Having a system that can replicate that process can help reduce costs and make it so that knowledge is more readily available throughout an organization, being used in different ways for different teams.4

A major challenge is that systems need to be able to adapt to unpredictability. Data is constantly changing. Some data is difficult to understand or explain, while other information is relatively straightforward. Often multiple experts are needed to address an issue. Another challenge is that experts don’t always communicate the same way; they might express themselves verbally, through visualizations or by demonstration.

In addition to requiring an understanding of AI and machine learning, knowledge engineering also needs an understanding of human behavior and computer programming.5 Knowledge engineers are responsible for connecting AI with the experts, whether they’re in business, science or medicine. As the facilitator of an expert’s knowledge to the final product, a knowledge engineer is someone who builds and maintains personal connections, so having the communication skills and soft skills to work alongside the experts is critical.

What’s important to know about knowledge engineering?

One of the biggest barriers to knowledge engineering is known as “collateral knowledge.” This is information that may not be considered immediately relevant to solving the program but is still needed to make a final decision. Collateral knowledge is not particularly definitive or clear cut. Humans’ nonlinear thought process makes replacing the expert themselves extremely difficult, if not entirely impossible.

People don’t use linear lines of thought when making a decision, so replicating their thought process is extremely difficult. Knowledge engineers realized that they couldn’t duplicate intuition or gut instincts. Often, solutions to problems are gained through previous experiences or learning from past mistakes. For many organizations, it’s not worth the cost or storage needed to create a system smart enough to handle all of that data.

Recently, knowledge engineers have pursued a different approach. They try to build a system that reaches the same answer but doesn’t necessarily use the same information or logic a human does. Essentially, they leave our nonlinear thinking behind. They cannot replicate human thought, but systems can learn to reach similar conclusions as the experts. When the two don’t match, AI engineers can go in and update the system. The model might get more complex, to the point where even the engineer isn’t sure how it’s reaching its correct answer. The eventual goal is that the systems reach the point where they are even smarter than the experts.6

Gain the Expertise at CWRU

Knowledge engineering requires high-quality data expertise and an in-depth knowledge on problem-solving methods. These AI engineers must be able to develop systems that can be used in different situations and for different uses. Knowledge engineering is about controlling the data, inputs and rules being put into AI systems, excluding what is and isn’t relevant.7

The field needs people who know how to adapt, react and evolve with the latest technologies and practices, because an engineer is needed to not just build systems but maintain them. These engineers also need the leadership and communication skills to work alongside experts in a number of industries.

Finding the right graduate degree in engineering can set you on that course. Our online Master of Science in Systems and Control Engineering prepares students to enter a number of in-demand engineering fields by gaining the technical tools and managerial experience to succeed. Learn more about our curriculum or start your application today.


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