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Unit 2 — Definition and Importance of Knowledge

Lesson 15 of 34 in the free Artificial Intelligence notes on Siksha Sarovar, written by Rohit Jangra.

Definition and Importance of Knowledge

Knowledge is organised information combined with understanding — facts, rules, and relationships that a system can use to reason, explain, and make decisions, as opposed to raw, unstructured data.

Data, Information and Knowledge

LevelDescription
DataRaw, uninterpreted facts or symbols
InformationData organised and given context/meaning
KnowledgeInformation combined with rules, experience, and the ability to act on it

Why Knowledge Matters in AI

A system without represented knowledge can only compute — it cannot reason. Knowledge representation (KR) is what lets an AI system:

CapabilityEnabled by knowledge representation
ReasoningDeriving new facts from known facts (inference)
ExplanationJustifying why a conclusion was reached
LearningIntegrating newly learned facts with what is already known
CommunicationExchanging facts and conclusions with humans or other systems
PlanningReasoning about future states/actions before acting

Types of Knowledge Used in AI Systems

TypeDescriptionExample
Declarative knowledgeFacts about the world — what is true"Water boils at 100°C at sea level"
Procedural knowledgeHow to do something — how to actAn algorithm for solving a quadratic equation
Heuristic knowledgeRules of thumb, expert judgement"If the patient has fever and rash, consider measles"
Structural knowledgeRelationships among concepts"A car IS-A vehicle; a car HAS-A engine"
Meta-knowledgeKnowledge about knowledge — what a system knows and doesn't know"This rule is more reliable than that rule"

Importance for Expert Systems (preview)

Every expert system (Unit 4) is, at its core, a large body of well-represented domain knowledge plus an inference engine that reasons over it — which is exactly why knowledge representation is studied as its own topic before expert systems are introduced.