Knowledge engineering is a field of artificial intelligence (AI) that aims to come up with data rules that would allow machines to replicate the thought processes of human experts. It has to do with developing knowledge-based systems such as computer programs that contain massive amounts of data about rules and solutions applicable to real-life issues.

Knowledge engineering defines how a conclusion or decision was reached to solve a complex problem that usually requires a human expert to accomplish.

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Knowledge engineering contributes valuable information about rules to expert systems that make use of expandable knowledge-based learning. Knowledge engineering systems often use machine learning (ML) to learn from real-life human experiences.

The Need for Knowledge Engineering

Today, knowledge engineering is a critical component of decision support software. Its application allows significant reductions in costs and fewer delays in implementation because knowledge is quickly captured and stored in a knowledge base. Such a database can then be used to spread know-how within an organization without downtime, applied for various purposes, and create more intelligent systems that perform highly complicated tasks.

We need knowledge engineering because most human experts often have difficulty recalling and explaining implied knowledge. They may also face some challenges when it comes to deciding which information to share.

The Principles of Knowledge Engineering

Knowledge engineering systems, like any other AI machines, are not perfect. Some require humans to identify processes so that extensive knowledge bases can be compiled for the devices to resolve complicated matters. As such, knowledge engineering systems need to:

1. Understand different types of knowledge

Knowledge engineers must keep in mind that there are various kinds of knowledge that they must represent when developing systems. They must distinguish between static and dynamic and tacit and explicit knowledge. When developing their knowledge bases, they must do so by converting abstract to concrete know-how and vice versa.

2. Determine different types of experts and expertise

Like knowledge, there are also multiple representations of experts and expertise. There must be clear-cut interpretations as to how experts can articulate understanding. Some can do so verbally while others do so graphically or through skills demonstrations. Another challenge is that most experts would have varying levels of recalling information. Some can remember information better during interviews, whereas others do better with demonstrations. To overcome this diversity, most knowledge engineers pool data from various sources.

3. Recognize the different classifications of knowledge

In most cases, human experts often use or classify their knowledge or expertise in various ways. For example, a sanitary engineer would view plumbing differently compared with a plumber who does the actual job. Most of the time, the categorization depends on the tasks they are currently doing. As such, knowledge engineers must be able to classify and adapt knowledge based on the issues they are supposed to address. Often, they need to come up with criteria to ensure that the data they will input would produce the expected solutions. Based on the example given above, the input must include theoretical knowledge coming from a sanitary engineer alongside practical know-how from a plumber.

All these principles must be correlated and structured in a way that is enough to create a well-defined method of creating a knowledge base that can be used depending on the needs of an organization.

Knowledge engineering must be continually modified and extended to ensure that each field is well-represented, including both theoretical and practical knowledge, so more people can benefit from the “transfer” of learning.