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To extract structured domain knowledge, provide raw source documents to the extractor to generate atomic, composable notes with reasoning and citations. This process converts unstructured material into categorized core notes, enrichment tasks, and cross-links.

Organizing daily logs into modular vault notes requires applying a configurable derivation manifest to queue and process raw items. This yields composable notes connected to related topics and enrichment tasks for navigability.

Knowledge extraction handles duplication by identifying when a source adds value to existing notes and generating specific enrichment tasks. This prevents redundant notes by cross-linking new extractions to established domain topics.

Yes, you can configure the extraction queue to process specific categories. The derivation manifest supports structured extraction for core notes, validations, tensions, open questions, and enrichment tasks.

Yes, this knowledge extraction method works without external dependencies. It operates internally to process raw source material, relying solely on its configurable derivation manifest and queue to generate atomic notes.

You should use a derivation manifest for structured knowledge extraction when you need to turn unstructured inbox items or research notes into retrievable, composable domain knowledge with categories like validations and open questions.