AI vs Human Knowledge Engineering
The Comparison
When transforming interview data into formal ontologies, AI and human knowledge engineers produce systematically different outputs.
AI-Generated Ontologies
Strengths:
- Higher structural consistency scores
- More standardized vocabulary
- Controlled terminology (fewer ad-hoc terms)
- Faster production time
Weaknesses:
- Consistently smaller (fewer concepts captured)
- Low instance counts (defaults to type hierarchies)
- 19-32% hallucinated content (factually correct but untraceable)
- Systematic omissions from content moderation
- Poor hierarchical alignment with reference structures
Human-Generated Ontologies
Strengths:
- Richer content (more concepts, more instances)
- Better hierarchical alignment
- Captures domain nuance
- Can encode tacit understanding
- Validates against expert intuition
Weaknesses:
- More variable quality
- Less standardized terminology
- Takes longer to produce
- Requires expensive expertise
Key Differences
| Dimension | AI | Human |
|---|---|---|
| Structural metrics | Higher | Lower |
| Semantic richness | Lower | Higher |
| Instance coverage | Poor | Better |
| Vocabulary control | Tight | Variable |
| Traceable content | 68-81% | ~100% |
| Production speed | Fast | Slow |
When Each Applies
Favor AI when:
- Speed matters more than depth
- Structure and consistency are priorities
- Domain is well-documented in training data
- Output will be validated by humans anyway
Favor humans when:
- Semantic accuracy is critical
- Domain has specialized vocabulary
- Coverage of specific instances matters
- Tacit knowledge needs encoding
- High-stakes decisions depend on the output
The Takeaway
The comparison reveals a fundamental tradeoff: structural cleanliness versus semantic richness. Current AI optimizes for the former; expertise requires the latter. This isn’t a capability gap that scaling will obviously close, it reflects different optimization targets.
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