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Knowledge Engineering – Definition, Concept, Principals, and More

Knowledge Engineering Definition

Knowledge Engineering is the branch of Artificial Intelligence dedicated to studying, designing, and developing Expert Systems. We are talking about software created to solve problems by emulating the mind of a person who is an expert in a specific field. For this, he learns business rules and techniques such as decision trees to mimic the knowledge of an expert in a field.

Concept of Knowledge Engineering

knowledge engineering

Knowledge engineering is part of Artificial Intelligence, and its purpose is the design and development of expert systems. The job of knowledge engineers is to extract knowledge from human experts in a given area and encode that knowledge management software to process it.

To do this, it relies on instructional methodologies and Computer and Information Sciences, trying to represent human knowledge and reasoning in a given domain within an artificial system.

The problem is that knowledge engineering is not an expert in the field they are trying to model. In contrast, the expert on the subject has no experience modeling their knowledge (based on heuristics) to be represented generically in a system.

Knowledge engineering encompasses the scientists, technology, and methodology necessary to process command. Its objective is to extract, articulate and computerize the knowledge of an expert.

The Principles of Knowledge Engineering

Knowledge engineering systems, like any other artificial intelligence machine, are not perfect. Some require humans to identify processes to compile extensive knowledge bases for devices to solve complex problems. As such, knowledge engineering systems must:

1. Understand Different Types of Knowledge

Knowledge engineers should keep in attention that they should represent several types of knowledge when developing systems. They must distinguish between static and dynamic learning and tacit and explicit knowledge. In developing their knowledge bases, they must convert abstract knowledge into concrete expertise and vice versa.

2. Identify the Different Types of Experts and Knowledge

Like knowledge, there are also multiple representations of experts and expertise. There must be clear interpretations of how experts can articulate understanding. Some can do this verbally, while others do it graphically or through skill demonstrations.

Another challenge is that most experts would have different levels of information research. Some may remember information better in interviews, while others are more successful in demonstrations. To overcome this diversity, most knowledge engineers combine data from a variety of sources.

3. Recognize the Different Classifications of Knowledge

In most cases, human experts use or categorize their knowledge or experience in various ways. For example, a sanitary engineer would view plumbing differently than a plumber who does the actual work. Most of the time, categorization depends on the tasks you are currently performing. As such, knowledge engineers must be able to classify and adapt knowledge according to the problems they are supposed to solve.

Often, they must come up with criteria to ensure that the data they enter will produce the expected solutions. Based on the example given above, the entry should include the theoretical knowledge of a sanitary engineer and practical knowledge.

All of these principles must be interrelated and structured in such a way as to create a well-defined method of building a knowledge base that can use according to the needs of an organization.

How Knowledge Engineering Use in a Company?

Among other things, Knowledge Engineering can develop Expert Systems that streamline the internal functioning of a company and, therefore, increase productivity. Thus, an Expert System can take care, for example, of:

  • Internal audits.
  • Accounting.
  • Task management.
  • Customer Support.
  • And also, automation of repetitive processes.

Process of Knowledge Engineering

knowledge engineering

In terms of its part in artificial intelligence (AI), knowledge engineering is the process of sympathy. The knowledge engineering process includes acquiring, representing, and validating learning, inference, explanation, and rationale.

Expert systems and algorithms are examples that form the basis for the representation and application of this knowledge. And also, she was representing human knowledge in data structures, semantic models, conceptual diagrams of data with the natural world, and heuristics, which lead to solutions to all problems encountered in AI.

The amount of collateral knowledge can be considerable depending on the task. Various advancements in technology and technical standards have helped to integrate data and make it accessible. These include the semantic web, an extension of the current web.

Information has a well-defined meaning; cloud computing provides access to large amounts of computing resources; and also opens datasets, datasets freely available for anyone to use and republish. These advances are crucial for knowledge engineering because they accelerate the integration and evaluation of data.

Why is Knowledge Engineering Important?

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

Businesses need to be able toward handle increasing amounts of info at quicker speeds. They can help usage machine learning and algorithms to identify ways to improve productivity and quality, but these systems ultimately need humans to take over the decision-making process.

These are the problems that an expert must solve. Having a system that can replicate this process can help reduce costs, make knowledge more available throughout the organization, and be used differently by different teams.

A significant challenge is that systems must be able to adapt to unpredictability. The data is constantly changing. Some data is difficult to comprehend or explain, while other information is relatively straightforward. Often, more than one expert is needed to resolve a problem. Another challenge is that experts do not always communicate in the same way; They can be expressed verbally, through visualizations or demonstrations.

In addition to requiring an understanding of artificial intelligence and machine learning, knowledge engineering also needs an understanding of human behavior and computer programming.
Knowledge engineers are responsible for connecting artificial intelligence to experts, whether in business, science, or medicine.

As an expert knowledge facilitator for the final product, a knowledge engineer builds and maintains personal relationships. It is therefore essential to have the communication skills and the soft skills to work alongside the experts.

Conclusion

Knowledge Engineering consists of generating new knowledge that did not exist before, from the information contained in the documentary databases and by crossing the content of the documents. And also, it starts from the actor-network theory since it reveals networks and generates new ones.

It also uses the Translation-Theory since, through the approach and relationship of actors, a molding or translation produce to take the statements or modalities to new stages of evolution.

Also Read: What is an Expert System? – Examples, Features, and More

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