Refers to facts and info that describe the world, answering the what type of knowledge.
Example: Knowing “Paris it the capital of France”
This knowledge is often stored in databases or knowledge bases and expressed in logical statements, forming the foundation for more complex reasoning and problem-solving in AI systems.
Procedural knowledge
The knowledge of how to perform tasks and procedural processes, answering the how type of questions.
Example: Steps to solve a mathematical problem or the procedure to start a car.
This knowledge is embedded in algorithms or control structures, enabling AI systems to execute tasks, perform actions and solve problems step-by-step.
Meta knowledge (knowledge about knowledge)
It is understanding which types of knowledge to apply where
Example: Knowing which algorithm to use to solve the problem at hand
Crucial for systems that need to adapt or optimize their performance, meta-knowledge helps in selecting the most appropriate strategy or knowledge base for a given problem.
Heuristic knowledge
It includes rules of thumb, educated guesses, and intuitive judgements derived from experience
Example: using an educated guess to approximate a solution when time is limited.
Often used in problem solving and decision making processes where exact solutions are not feasible, helping AI systems to arrive at good enough solutions quickly
Structural knowledge
It refers to the understanding of how different pieces of knowledge are organized and related to each other
Example: Understanding the hierarchy of concepts a taxonomy or the relationships between different entities in a semantic network
This knowledge is essential for organizing information within AI systems, allowing for efficient retrieval, reasoning and inferencing based on the relationships and structures defined.