Learnedknown

Learnedknown is a term used in knowledge representation and artificial intelligence to describe a framework that combines information learned from data with knowledge that is known from authoritative sources. In this view, models derive generalizations from training data while explicit facts are stored and validated in curated knowledge bases. The term emphasizes the coexistence and interaction of these two modalities of knowledge within a single system.

The phrase is commonly used in discussions of hybrid AI and knowledge-based systems. It is not a formal standard and may be applied in varying ways by researchers, but it generally functions as a descriptor for approaches that integrate learned inference with externally validated information. The concept helps address gaps where purely learned models may lack grounding or verifiability, and where purely symbolic systems may lack adaptability to new data.

Core components of learnedknown include the learned component, such as neural networks or probabilistic models that discover patterns from data, and the known component, such as fact databases, ontologies, and curated knowledge graphs. Interaction patterns involve grounding outputs in known facts, enforcing consistency with knowledge bases, and designing update mechanisms when sources are revised or expanded. Evaluation typically considers both predictive performance and alignment with established knowledge, along with measures of provenance and traceability.

Applications span question answering, fact checking, robust search, and robotics, where systems benefit from the efficiency of learning and the trustworthiness of known information. Challenges remain in provenance, uncertainty estimation, drift, and balancing flexibility with verifiability.