Schema-Based vs Schema-Free Extraction

Two paradigms for extracting structured knowledge from text, each with different tradeoffs:

Schema-based extraction operates under explicit structural guidance. A predefined ontology constrains what entities and relations the system looks for. This emphasizes normalization, structural consistency, and semantic alignment. The system knows what it’s looking for before it starts looking.

Schema-free extraction transcends predefined templates. The system discovers entities and relations directly from text without prior schema constraints. This prioritizes adaptability, openness, and exploratory discovery. The structure emerges from the data rather than being imposed upon it.

Neither is universally better. Schema-based extraction produces cleaner, more consistent output but can’t find what it wasn’t designed to look for. Schema-free extraction captures more of what’s actually in the data but produces messier, less normalized results.

The most interesting recent work explores the middle ground, dynamic schemas that adapt based on what’s being extracted rather than remaining fixed throughout.

Related: 06-atom—static-vs-dynamic-schemas, 06-atom—three-bottlenecks-kg-construction