Knowledge Base Tagging

Problem it solves

Enterprise documents are often stacked by title, trapped in single-folder hierarchies, or missing consistent tags, which makes retrieval, maintenance, and cross-linking difficult. Knowledge Base Tagging works on existing documents and knowledge-base content; it does not write new docs, replace search engines, or design databases, but organizes content into a searchable, linkable structure.

How it works

• Taxonomy design: Defines dimensions such as topic, document type, audience, and difficulty, then proposes first-, second-, and third-level tags with required, optional, or mutually exclusive rules.
• Batch tagging: Uses titles, abstracts, or full text to assign primary and secondary tags; for stable quality, keep batches under 50 documents.
• Relationship mapping: Identifies prerequisite, related, or advanced links between documents, reducing the limits of single-folder classification.
• Directory output: Generates a visual directory tree and a tag usage guide that can be imported into Notion, Confluence, or other knowledge-management tools.

Boundaries

Best for organizing existing documents, diagnosing messy tag systems, and mapping legacy docs to a new scheme. Not suitable for authoring new documentation, tuning full-text search, database schema design, or file backup. Document content is required, and the generated tag scheme should be reviewed before importing into production systems.