Logical representation
- Involves using formal logic systems like propositional predicate logic to represent knowledge in a structured, precise and unambiguous way.
- It allows AI systems to perform reasoning by applying rules of inference to derive conclusions from known facts. It is commonly used in applications that require rigorous and consistent decision making, such as proving theorems and rule-based systems.
Semantic Networks
- It is a graphical representation of knowledge where nodes represent concepts and edges represent relationships between those concepts.
- It is used to model hierarchical relationships and associative relationships.
- They help AI systems understand the connections between different concepts and perform tasks like inference, classification and ontology mapping.
Frames
- These are data structures that encapsulate knowledge about objects, situations or events in a structured format. Each frame contains attributes and their associated values, which can include default values, constraints, and even procedural knowledge.
- Frames are used to represent stereotypical objects or situations, allowing AI systems to make inferences based on the structure and relationships within frames.
- Example: The frame of a car may include slots for make, model, colour and owner along with other rules for filling information.
Production rules
- These are “if-then” statements that express knowledge in the form of conditions and corresponding actions. They are a key component of rule-based systems.
- Production rules are used in expert based systems where they form the basis for decision making and problem solving.
- When the condition (if-part) of a rule is met, the corresponding action (then-part) is executed, enabling the AI system to derive conclusions, perform tasks or generate responses.
Ontologies
- An ontology is a formal representation of a set of concepts within a domain and the relationships between them.
- Ontologies provide a shared vocabulary and a common understanding of a domain, which can be used by both humans and AI systems.
- They are widely used in knowledge management, semantic web technologies and Natural Language Processing.
- They enable AI systems to understand the context of the information, perform reasoning across different domains and facilitate interoperability between systems. For example, an ontology for the medical domain might define relationships between diseases and its medications and treatments, helping AI systems diagnose illnesses or suggest treatment options.