Embeddings vs. Taxonomies for Skills Matching
Overview
Two approaches to connecting skills with opportunities: vector similarity search and structured taxonomy traversal. Each captures different aspects of “relatedness.”
Embedding-Based Matching
How it works: Encode skills and job requirements as vectors. Match based on cosine similarity in embedding space.
Strengths:
- Handles synonyms and paraphrases naturally (“project management” ≈ “managing projects”)
- Requires no manual ontology construction
- Adapts to new terminology through retraining
- Captures semantic similarity even across different phrasing
Weaknesses:
- Can’t distinguish types of relationships (prerequisite vs. adjacent vs. broader)
- Similarity isn’t transitivity (A similar to B, B similar to C doesn’t mean A similar to C in meaningful ways)
- Struggles with contextual disambiguation without additional signals
- Black box, hard to explain why two skills matched
Taxonomy-Based Matching
How it works: Navigate explicit hierarchical and associative relationships between classified concepts.
Strengths:
- Relationships are typed and explicit (broader, narrower, related, required-for)
- Supports reasoning about skill portability through reusability levels
- Enables path-based queries (“what skills adjacent to X are required for occupation Y?“)
- Human-interpretable structure
Weaknesses:
- Coverage limited to what’s been classified
- Maintenance-intensive as domain evolves
- Brittle to terminology variation (“project management” might not find “PM skills”)
- Requires upfront ontology investment
Key Differences
| Dimension | Embeddings | Taxonomies |
|---|---|---|
| Relationship types | Implicit similarity | Explicit typed relations |
| Coverage | Anything encodable | Only classified concepts |
| Maintenance | Retraining | Manual curation |
| Explainability | Low | High |
| Handling new terms | Natural | Requires addition |
When Each Applies
Use embeddings when: You need fuzzy matching, have diverse/messy input data, can’t afford taxonomy maintenance, or need to bootstrap quickly.
Use taxonomies when: Relationship types matter, explainability is required, skill portability needs explicit modeling, or you’re integrating with systems that expect structured data.
Use both when: You need the coverage and flexibility of embeddings plus the precision and explainability of taxonomies. ESCO demonstrates this hybrid approach.
Related: 07-molecule—vectors-vs-graphs