Prompt Design Dimensions for Knowledge Validation

Overview

When using LLMs to validate knowledge artifacts (ontology mappings, entity matches, taxonomy placements), prompt design significantly affects performance. Three dimensions matter most.

The Dimensions

1. Natural Language Friendliness (NLF)

Structured prompt: Technical formatting, explicit field labels, formal ontological language.

Source entity: "alveolus epithelium"
- Direct ontological parent: lung epithelium
Target entity: "Alveolar_Epithelium"  
Are these entities ontologically equivalent?

Natural language-friendly prompt: Conversational framing, human-readable.

We have two entities from different ontologies.
The first is "alveolus epithelium," which belongs to "lung epithelium."
The second is "Alveolar_Epithelium," which belongs to "Epithelium."
Do they mean the same thing?

Finding: NLF prompts consistently outperform structured prompts. LLMs respond better to questions framed the way humans naturally ask them.

2. Extended Context (EC)

Basic context: Direct parent only. Extended context: Multiple levels of hierarchical ancestors.

Finding: Extended context adds marginal value. The additional structural information doesn’t compensate for longer prompts and potential confusion.

3. Synonyms (S)

Without synonyms: Entity labels only. With synonyms: Explicit inclusion of alternative names.

"Alveolar_Epithelium", also known as "Lung Alveolar Epithelia", 
"Alveolar Epithelium", "Epithelia of lung alveoli"

Finding: Synonyms significantly improve performance. Explicit lexical variations surface knowledge the LLM already has but might not activate with the primary label alone.

Optimal Configuration

P^NLF_S: Natural language-friendly with synonyms, without extended context.

This configuration achieved the best Youden’s Index across nine ontology matching tasks.

When to Use This Framework

• Designing prompts for entity matching/validation
• Building LLM-based knowledge validation pipelines
• Comparing prompt strategies systematically
• Training data annotation with LLM assistance

Limitations

• Tested primarily on biomedical ontologies
• Binary validation tasks only (not generation)
• Results may vary with domain-specific terminology

Related: 05-molecule—targeted-llm-intervention-pattern, 05-atom—llm-as-oracle-vs-aligner