Definition
What this term means
A search approach that understands the meaning and intent behind a query rather than simply matching keywords. Semantic search uses NLP, embeddings, and knowledge graphs to interpret what a user is actually looking for, even if their query uses different words than your content. This technology powers both modern search engines and AI-assisted retrieval systems.
Why it matters
The business impact
Semantic search means you can no longer rely solely on keyword matching to be found. AI systems evaluate your content based on conceptual relevance, topical depth, and entity relationships, not just whether you used the right keywords. Brands that create content with clear entity relationships, comprehensive topic coverage, and natural language aligned with user intent will outperform keyword-stuffed competitors in both traditional and AI-powered search.
Used in context
How you might use this term
“A company optimised their content for the keyword 'cloud migration services' but was not appearing for related queries like 'move our infrastructure to the cloud'. After shifting to a semantic approach, building entity relationships and covering the topic comprehensively, they began appearing for a much wider range of semantically related queries across both Google and AI platforms.”
Related terms
Explore connected concepts
Embeddings
Dense numerical representations (vectors) that capture the semantic meaning of text. When AI systems convert your content into embeddings, they create mathematical fingerprints that encode what your content is about, its context, and its relationships to other concepts. These vectors are used to measure semantic similarity, enabling AI systems to find content that is conceptually relevant to a query, even if it does not share exact keywords.
Vector Index
A specialised database that stores and searches embeddings for fast semantic retrieval. Vector indexes are the infrastructure behind RAG systems. When an AI assistant needs to find relevant information to answer a query, it searches the vector index for content embeddings that are semantically closest to the user's question. Popular vector databases include Pinecone, Weaviate, and Qdrant.
Natural Language Processing
The branch of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. NLP encompasses a wide range of technologies, from basic text analysis and sentiment detection to the sophisticated language understanding that powers AI assistants and generative search engines. It is the foundation upon which all modern AI language tools are built.