Infrastructure for Auto-Tagging & Taxonomy Management

Auto-tagging requires a formally documented taxonomy that the NLP system can apply as a controlled vocabulary. The ps-km baseline has taxonomy defined — industry codes, service line classifications, deliverable types, methodology components. For auto-tagging to function, these taxonomy terms must be documented as explicit, findable definitions (not just folder names) that the model can use as classification targets. The system must know that 'Digital Transformation' maps to a specific taxonomy node, not just a folder label in one practice.

Auto-tagging needs documents to be systematically captured into repositories where the NLP pipeline can process them. Mandated upload workflows with required metadata fields ensure documents arrive in the system with at least basic context (project name, practice area, date). This systematic deposit enables the auto-tagger to process each document at upload time. Additionally, user-generated tags captured during upload serve as training signals for improving tagging model accuracy.

Auto-tagging and taxonomy management require formal ontology — not just a flat list of tags but a structured hierarchy of concepts with parent-child relationships, synonyms, and cross-references. The NLP system must understand that 'Supply Chain Optimization' is a child of 'Operations' and a sibling of 'Logistics Management,' and that a document tagged with the child term is also retrievable via the parent. Without formal ontology, auto-tagging produces flat tag lists that don't capture the semantic relationships needed for taxonomy consistency checking and gap identification.

The auto-tagging pipeline must programmatically access document repositories to extract text, apply NLP classification, and write tags back to document metadata. Modern SharePoint and Confluence expose APIs sufficient for this read-write workflow. The system can retrieve document content, process it through the tagging model, and update metadata fields without manual intervention. However, binary format extraction (docx, pptx) requires document parsing pipelines that add latency to the tagging workflow.

Taxonomy evolves as new service offerings, industries, and methodologies emerge. Auto-tagging accuracy degrades if the taxonomy it applies becomes stale — new content about 'Generative AI in Operations' gets misclassified because the taxonomy predates that category. Event-triggered taxonomy updates (when a new practice is launched, when a major methodology refresh occurs) keep the tagging model aligned with firm vocabulary. The system should flag new term candidates from emerging content patterns for taxonomy governance review.

Auto-tagging primarily needs point integration between the NLP pipeline and the document repository — read content, write tags back. This is a contained workflow. Integration with CRM or PSA project context would improve tagging precision (knowing this document belongs to a healthcare project helps disambiguate 'operations' vs. 'clinical operations'), but the core auto-tagging function works with standalone repository access. The taxonomy management output (gap identification, new term suggestions) is consumed by knowledge managers, not by downstream integrated systems.