Semantic Similarity Extraction Pattern
Context
You have a structured knowledge resource (ontology, schema, documentation) and natural language requirements describing what you need from it. You want to automatically identify which concepts are relevant without manually reviewing the entire resource.
Problem
Manually searching large ontologies or schemas is tedious and requires expertise. You need an automated way to surface relevant concepts based on requirements expressed in plain language.
Solution
- Encode requirements as natural language sentences that clearly express the intended representation
- Extract textual metadata from the knowledge resource (labels, definitions, comments, descriptions)
- Embed both into a shared vector space using sentence transformers
- Compute similarity between each requirement and each concept
- Apply threshold to identify candidates
- Extract modules around the high-scoring concepts using structural methods
Consequences
Benefits:
- Scales to large knowledge resources
- Doesn’t require domain expertise to run
- Surfaces concepts you might not have thought to look for
Limitations:
- Favors well-documented concepts over well-designed ones (annotation quality bias)
- Finds textual similarity, not structural relationship
- Requirement phrasing affects results
- Can’t identify patterns distributed across multiple sources
When to Apply
Use when you need a first-pass filter on a large knowledge resource. Don’t treat results as definitive, they’re candidates for human review, not final answers.
Variations
- Use multiple paraphrases of requirements to reduce phrasing bias
- Combine with structural analysis to verify relationship patterns
- Apply cross-ontology matching to find shared patterns