Representation Learning for Taxonomy Maintenance
Using learned embeddings to assist with taxonomy evolution, gap identification, and consistency checking. AI augmentation of knowledge engineering tasks.
Applications
Gap Detection: Identify concepts that should exist but don’t
- Embed existing taxonomy terms
- Find clusters in usage data without taxonomy coverage
- Suggest new terms or categories
Inconsistency Detection: Find structural problems
- Similar terms in distant taxonomy branches
- Parent-child pairs with low semantic similarity
- Synonyms not linked as equivalents
Evolution Assistance: Support taxonomy updates
- Suggest where new terms belong
- Identify candidates for merging or splitting
- Predict impact of structural changes
Method Patterns
- Embed taxonomy terms using language models
- Embed usage data (queries, tagged content)
- Analyze alignment and gaps
- Human review of suggestions
Limitations
- Embeddings capture statistical patterns, not domain logic
- Novel concepts may have poor embeddings
- Human expertise still required for final decisions
Related: 02-molecule—taxonomy-design, 06-molecule—ontology-design-patterns