Knowledge Representation

Knowledge representation studies how to make information explicit and structured so computers can connect facts, draw conclusions, and explain what they know. Common approaches include formal logic and OWL ontologies, RDF knowledge graphs and schemas, and learned embeddings that support tasks such as graph completion and ontology matching. Increasingly, systems combine these representations with large language models for scientific or clinical question answering and persistent agent memory, where provenance, time, and context affect whether a fact applies. A central challenge is balancing expressive, interoperable representations with efficient reasoning, while ensuring that automatically extracted or generated knowledge is accurate, traceable, and open to correction.