Knowledge Representation is how an AI system stores, manipulates, infers, and expresses knowledge. It forms the basis for machine reasoning, enabling AI to simulate human-like capabilities.


Let’s say, you’re a detective trying to solve a mystery. To solve it, you collect clues (knowledge) and link them together (representation). You’ll remember where you found each clue and how they helped you reach a conclusion. Knowledge Representation in AI is like that. It’s about collecting information and storing it in a way to solve problems or answer questions.

In-depth explanation

Knowledge Representation aims to duplicate human intelligence and perception by structuring knowledge for an AI program to understand and use. These structures can take several forms: logical, semantic, and network-based schemas are common.

Logical schemas involve expressing data in a mathematical, formal logic structure. The benefit of such schemas is the predictability and accuracy of outcomes - if the logic is sound, a machine can draw correct and precise conclusions.

Semantic networks, a substantial idea in artificial intelligence, graphically represent knowledge as a sequence of interconnected nodes and edges. This representation is analogous to how one might visualize a mind map or concept network. Its main advantage is readability and ease of modification.

Frames, another method of representation, draw inspiration from the classes and instances found in object-oriented programming. A framework is an abstract description that includes a set of attributes (slots) and values for those attributes.

Rule-Based systems are another form of representation mainly used in expert systems. Here, the knowledge is expressed in the form of conditional rules, which are typically used in situations where heuristic knowledge can be applied straightforwardly.

Regardless of the form they take, a successful knowledge representation should possess explicitness, inferential adequacy, inferential efficiency, and learnability. Explicitness highlights the need for the representation to convey enough useful information. Inferential adequacy and efficiency refer to its ability to support necessary inference and to do so quickly. Finally, learnability underlines its ability to adapt and incorporate new information.

In summary, knowledge representation is crucial for an AI system to make sense of the world and reason about it. The choice of representation depends on the specific use case and the kind of data the AI system will handle.

Ontology, Semantic Network, Frames, Rule-Based Systems, Propositional Logic, Fuzzy logic, Neural Networks, Cognitive Architecture, Expert Systems.