AI Reference 019 Classical AI & Machine Learning Wikipedia guided Primary sources linked

Knowledge Representation

Definition

Knowledge representation studies how facts, relations, rules and concepts can be encoded so a computer system can reason over them.

What is knowledge representation?

Knowledge representation is the study of how facts, concepts, relationships and rules can be encoded so a computer can use them. The representation determines which questions are easy to ask and which types of reasoning the system can perform.

Common forms

Classical forms include formal logic, semantic networks, frames, production rules and later ontologies and knowledge graphs. A medical expert system might represent symptoms, diseases and diagnostic rules; a knowledge graph represents entities and labeled relationships.

Why representation matters in modern AI

LLMs learn distributed numerical representations rather than relying only on explicit symbolic databases. Even so, production AI often combines learned models with structured databases, schemas, ontologies and graphs because organizations need exact identifiers, relationships and provenance that a generative model should not guess.

Related terms, defined

Classical AI & Machine Learning Expert Systems Expert systems are narrow rule-based programs that separate a knowledge base from an inference mechanism to reproduce parts of specialist decision-making. Classical AI & Machine Learning ELIZA and Early Conversational Programs ELIZA, created by Joseph Weizenbaum in the 1960s, used pattern matching and scripted text transformations to simulate conversation without modern statistical language understanding. Classical AI & Machine Learning SHRDLU and Language in a Blocks World SHRDLU, developed by Terry Winograd around 1968–1970, accepted natural-language instructions about a small simulated blocks world and combined language parsing with a symbolic world model. Classical AI & Machine Learning The AI Winters AI winters were periods in which enthusiasm and funding fell after systems failed to meet expectations or became economically unattractive. The term is especially associated with downturns in the 1970s and again in the late 1980s and early 1990s. Classical AI & Machine Learning Bayesian Networks A Bayesian network is a directed acyclic graph representing probabilistic relationships between variables using graph structure and conditional probability distributions.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

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