knowledge-graph

To organize scattered notes into a structured knowledge base, input your research materials to automatically extract MECE dimensions and generate a two-layer document structure. This separates knowledge definitions from instances for long-term maintenance.

Compiling research notes for product and company investigations is best handled by enforcing frontmatter-driven naming and extracting MECE dimensions. This approach indexes scattered information into a unified, long-lasting knowledge base with guided references.

MECE dimension extraction works by automatically categorizing your scattered research input into mutually exclusive and collectively exhaustive categories. This builds a structured two-layer knowledge document separating general knowledge from specific instances.

You do not need to manually format references when creating knowledge-base documents. The system uses a references directory with built-in templates and guidance to help you quickly structure overview, technical, and research dimensions.

Yes, you can use this approach for long-term maintenance of technical research documents because it separates the knowledge layer from the instance layer. It provides unified locating paths and reference guidance to ensure your documentation remains extensible.