What is Knowledge Engineering?

Knowledge Engineering (KE) is one of the extended areas of artificial intelligence (AI). With KE, technologists try to create the nearest human-like thought process of a human expert by inferring the use of rules on data. One can say that KE enables machines to identify and reach a conclusion by analyzing the structure of a task or any decision.

Many problem-solving methods keep piling to the collateral knowledge in today's data-driven world. Therefore, to resolve time-critical problems that need to be diagnosed by the system, KE builds, maintains, and uses knowledge-based systems.

Today, KE has become one of the most sought-after knowledge transfer experts who has revolutionized problem-solving using human-like thought processes to provide reasonable conclusions.

KE or the transfer process has been left behind in favor of a modeling process to approach intuitive thinking. Instead of following a step-by-step decision process, KE creates a system that reaches expert-like results without following the same path. KE thus eliminates some of the issues of tracing knowledge used for nonlinear thinking. Moreover, with the growing complexity of this model, the knowledge engineers still struggle to understand how conclusions are being reached ultimately. As a result, KE will exceed human expertise.

KE has already been integrated into decision support software following the groundbreaking result. As a result, many roles for specialized knowledge engineers have opened in various fields advancing human-like functions. Coupling KE models with natural language processing (NLP) and facial recognition, the world will see new heights in the advancement of AI.

Benefits of Knowledge Engineering

As a scalable decision support software tool, the general benefits of KE are:

Accelerated of decision making
Fair possibility to replicate expert decision making
Emulates the human decision process
Increases the availability of knowledge across organizations
Seeks model decision-making processes that domain experts regularly perform
Helps in creating a better expert system
Assembles different domain knowledge to address complex problems
Creates robust models

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