Knowledge Representation

Knowledge representation is the field of AI concerned with encoding information about the world in formats that machines can use for reasoning, inference, and problem-solving. Approaches range from symbolic methods like ontologies, semantic networks, and knowledge graphs to distributed representations like word embeddings and neural network activations in latent space. Effective knowledge representation must balance expressiveness with computational tractability, enabling systems to store complex relationships while supporting efficient queries. In expert systems, rule-based representations capture domain knowledge as if-then statements, while modern AI increasingly uses learned representations that emerge from training on large datasets. The integration of symbolic and neural approaches, sometimes called neurosymbolic AI, aims to combine the interpretability and logical rigor of symbolic knowledge representation with the pattern recognition strengths of deep learning.