The field of AI concerned with how to formally encode knowledge so machines can reason with it.
Knowledge Representation (KR) is a subfield of artificial intelligence concerned with how to formally encode knowledge about the world in a way that a computer system can use to solve complex problems. KR encompasses the design of formal languages and data structures for representing facts, rules, relationships, and uncertainty — from simple databases and decision trees to rich ontologies and probabilistic graphical models. The central challenge is capturing the right level of detail and expressiveness for the intended reasoning tasks.
Knowledge representation has experienced a renaissance as the limitations of pure statistical AI have become apparent. LLMs can generate fluent text but struggle with consistent logical reasoning — a problem that KR techniques directly address. The field of neurosymbolic AI is essentially the integration of modern deep learning with classical knowledge representation, combining the best of both paradigms. Enterprise AI architects are increasingly required to understand KR fundamentals.
Knowledge representation systems range from propositional logic (simple true/false facts) to first-order predicate logic (quantified statements about objects and their properties) to description logics (the basis of OWL) to probabilistic graphical models (Bayesian networks, Markov random fields). Each formalism trades expressiveness for computational tractability. Practical KR systems choose the formalism that is expressive enough to capture the required knowledge while remaining computationally feasible.
A medical AI system uses knowledge representation to encode clinical guidelines. Rules like 'If patient has Type 2 Diabetes AND HbA1c > 9% AND is not on insulin THEN recommend insulin therapy' are represented in a formal logic system. The AI can then apply these rules to patient data to generate evidence-based recommendations — with full traceability of the reasoning chain, unlike a neural network that produces recommendations without explanation.