Semantic Search
Definition
Semantic Search
Semantic search analyzes the meaning and context of queries rather than simply matching keywords.
Timothé Merle(Co-founder Hikoo - Expert AEO/GEO)
Updated on
TL;DR
Key Definition
Semantic search understands the intent behind a query. It can match "best task management tool" to your product even if you don't use those exact words on your site.
Importance
Why Does It Matter?
- AI uses semantic search to understand queries
- Allows ranking for concepts, not just keywords
- Reduces dependence on exact keyword matching
- Improves response relevance for users
- Foundation of RAG and embeddings
How Does It Work?
Semantic search uses language models to represent texts and queries in a vector space.
Embeddings
Each text is converted into a numerical vector capturing its meaning. Similar texts have close vectors.
Cosine Similarity
Similarity between two texts is measured by the angle between their vectors. The smaller the angle, the more similar they are.
Metrics
How Do You Measure It?
- Diversity of queries generating traffic
- Match between intents and visited pages
- Quality of your content embeddings
Pitfalls
What Are the Common Mistakes?
- Over-optimizing for exact keywords instead of concepts
- Ignoring semantic variations of queries
- Content too technical without explanatory context
What Should You Do First?
Follow these steps to get started.
- Identify key concepts in your domain
- Cover variations and synonyms naturally
- Use clear and contextual language
- Structure around user intents
- Avoid jargon without explanation
Examples
Conceptual Matching
A user searches "how to improve my presence on AI". Semantic search understands they mean GEO and suggests your article.