Linked from

The 13 pages that link to Knowledge representation, each with the reason it gives.

• Artificial intelligenceRelated: AI systems need representations that make relevant facts and relationships computable.

• Predicate logicRelated: Predicate logic represents facts and rules about entities and their relationships.

• Paraconsistent logicNarrower topic: Paraconsistency offers a way to represent contradictory facts in evolving knowledge bases.

• Symbolic artificial intelligenceNarrower topic: Symbolic AI begins by encoding facts, relations, and rules in machine-readable forms.

• OntologyRelated: Ontologies provide explicit categories and relations for representing domain knowledge.

• Expert systemRelated: It frames the choices involved in encoding a domain as facts and rules.

• Knowledge graphNarrower topic: Knowledge graphs are one way to represent structured information for computation.

• Semantic WebNarrower topic: The Semantic Web is a web-scale approach to representing machine-processable knowledge.

• Knowledge organizationRelated: Representing knowledge raises questions about which distinctions systems can express.

• Description logicNarrower topic: Description logic is a formal foundation for representing structured knowledge.

• Automated reasoningRelated: Reasoning engines draw conclusions from structured facts, rules, and ontologies.

• Larry SangerRelated: Sanger’s later projects and advocacy explored ways to make online knowledge more structured.

• Knowledge (artificial intelligence)Narrower topic: This is the broader discipline concerned with representing AI knowledge.