Leerschemas
Leerschemas are a class of data model representations that blend traditional schema constraints with patterns learned from data, enabling the schema itself to adapt as data evolves. The term is used in theoretical and experimental discussions within data management and AI to describe dynamic schema paradigms that balance stability with adaptability.
Core components typically include a schema template that encodes structural constraints such as field types and
Lifecycle and governance features often accompany leerschemas: initialization from an initial template, continuous data ingestion that
Applications span data integration, knowledge graphs, and natural language processing pipelines where data sources are heterogeneous
Relation to other concepts: leerschemas extend traditional schemas and schema inference by integrating learnable components, positioning
See also: Schema, Schema evolution, Ontology, Data integration, Knowledge graph.