Dense Semantic Search
Semantic search finds relevant information based on conceptual meaning and intent rather than matching exact string keywords.
Dense semantic search is an information retrieval technique that matches queries to documents based on their conceptual meaning in vector space rather than literal keyword overlap.
How Semantic Search works in practice
When a customer asks “Do you have accommodations for wheelchairs?”, semantic search matches content containing “wheelchair accessible ramps” and “ADA compliant elevators” without requiring keyword matches.
By projecting both query and documents into the same embedding space, it calculates cosine proximity to rank answers by true user intent.
How SiteMind implements Semantic Search
SiteMind uses semantic search to ensure customer queries phrased in informal language accurately find official answers across your documentation.
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Related Technical Concepts
Vector Embeddings
Vector embeddings convert text into high-dimensional numerical lists that capture semantic meaning, enabling computers to compare concepts rather than keywords.
Hybrid Search (Dense + Sparse Retrieval)
Hybrid Search merges dense vector search with sparse keyword matching to deliver superior accuracy across both conceptual and exact-term queries.
Cosine Similarity Thresholds
Cosine similarity measures the angle between two embedding vectors in multidimensional space to determine how closely their meanings match.
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