# Knowledge Representation **Domain:** Artificial Intelligence / Logic / Information Systems **Doc Type:** Canonical Concept Node **Maturity:** Developed **Related:** [[wiki/GOFAI (Good Old-Fashioned AI)|GOFAI (Good Old-Fashioned AI)]], [[wiki/Ontology|Ontology]], [[wiki/Encoded Domain Knowledge|Encoded Domain Knowledge]], [[wiki/Semantic Network|Semantic Network]], [[wiki/Provenance|Provenance]] --- ## Definition **Knowledge representation is the design of computational structures that make entities, distinctions and relations available for reasoning, retrieval or action.** A representation determines what a system can ask, compare, infer and explain. Representations may include rules, ontologies, graphs, feature structures, vectors, examples or trained parameters. These forms are not interchangeable. An explicit taxonomy supports auditable “type of” traversal; an embedding supports graded similarity; a corpus index supports attestation; a pronunciation lexicon supports phonological operations. ## Language-Engine Context The [[wiki/Symbolic Language Engine|Symbolic Language Engine]] treats English as several overlapping representations rather than one undifferentiated word list. Pronunciations, rhyme classes, word senses, taxonomic edges, trigger relations and poetic constraints answer different questions about the same text. This approach illustrates the principle in [[wiki/Encoded Domain Knowledge|Encoded Domain Knowledge]]: intelligence depends partly on which human distinctions have been made computable. It also introduces an evidence obligation. When a system returns a suggestion, the representation that produced it should remain identifiable. ## Key Insight **Representation is not neutral storage. It defines the system's visible world and the operations that count as intelligence within it.** ## See Also [[wiki/Ontology|Ontology]], [[wiki/Encoded Domain Knowledge|Encoded Domain Knowledge]], [[wiki/Semantic Network|Semantic Network]], [[wiki/Multi-Index Language Representation|Multi-Index Language Representation]], [[wiki/Word Embeddings|Word Embeddings]], [[wiki/Provenance-Sensitive Multiplicity|Provenance-Sensitive Multiplicity]] ## Sources / Provenance - Primary project account: [[projects/Ten Years Building a Symbolic Language Engine|Ten Years Building a Symbolic Language Engine]]. - Ronald Brachman and Hector Levesque, *Knowledge Representation and Reasoning* (2004). - Stuart Russell and Peter Norvig, *Artificial Intelligence: A Modern Approach*.