Knowledge Engineering is the aspect of artificial intelligence that focuses on creating knowledge-based systems. In essence, it involves designing AI software that can “reason” and integrate knowledge into processes.


Imagine you’re playing a game of chess. You have learned the rules, studied some strategies, and you’re making moves based on this knowledge. That’s what knowledge engineering does for artificial intelligence - it gives AI the rules (knowledge) it needs to make good “moves” or decisions.

In-depth explanation

Knowledge Engineering (KE) is a core element of artificial intelligence (AI) which involves designing, building, and implementing knowledge-based systems. These systems make use of structured information, better known as “knowledge”, to solve complex problems that would usually require a high level of human expertise.

In the realm of AI, knowledge is typically represented as a set of rules or a collection of facts about a specific domain. The concept is that by storing this knowledge, the system can make decisions, deliver recommendations, or process requests that are within its specified area.

The key activities in knowledge engineering include knowledge acquisition, knowledge representation, and knowledge validation.

  1. Knowledge acquisition: This involves collecting and structuring information from various sources including databases, documents, and human experts.

  2. Knowledge representation: This focuses on how to encode the acquired knowledge in a form that can be processed by the AI. Common methods include semantic networks, frames, rules, and ontologies.

  3. Knowledge validation: This stage ensures that the knowledge embedded in the system is correct, reliable, and efficient. Techniques such as consistency checking can be used to validate the knowledge.

One of the most well-known applications of KE is in building expert systems which are designed to mimic the decision-making ability of a human expert. For example, medical diagnosis systems that can suggest possible diseases based on a set of symptoms input into the system.

In recent years, the convention of hard-coded knowledge in KE has been supplemented with learning from data, also known as machine learning. This combination allows systems to have both a base of solid, reliable rules and the capability to learn and adapt with incoming data.

Expert Systems, Semantic Networks, Ontology, Machine Learning (ML),, Artificial Intelligence, Knowledge Representation, Knowledge Acquisition, Knowledge-Based Systems, Reasoning Systems