Semantic search and culture: when AI understands meaning, not just words
When a visitor calls your museum and asks "do you have anything on the Impressionists?", they do not expect to be told "we have nothing on the Impressionists" simply because the exact word does not appear in your brochure. If your brochure mentions "Monet and Light: the Legacy of Impressionism," the visitor rightly considers this relevant. A human makes this connection effortlessly. For decades, computer systems could not. They worked by exact keyword matching. Semantic search fundamentally changes this logic, allowing systems to understand the meaning of words and make connections based on signification rather than spelling.
By Artedusa
••9 min read01How keyword search works
Classical keyword search takes the word typed and searches documents for exact matches or direct grammatical variants. This system is simple but fundamentally limited. It does not understand that "exceptional closure" and "we will be closed" express the same idea, or that "student discount" and "special price for under-26s" designate potentially the same offer.
02How semantic search works
Semantic search compares meanings rather than words. It transforms texts into numerical representations, vectors, in a mathematical space where distance reflects proximity of meaning. "Museum opening hours" and "when is the museum open" end up close in this space despite sharing few words. When a visitor asks a question, it too becomes a vector, and the system finds the five closest segments in your knowledge base.
03What this changes for cultural institutions
Visitors formulate questions differently from how documents are written. They say "how much to get in?" not "individual adult admission tariff." Semantic search understands all formulations express the same need. It also bridges "wheelchair" and "reduced mobility" even though they share no words. In a multilingual context, it can match "opening hours" with the French brochure passage about "horaires d'ouverture."
04Relevance over exhaustiveness
Only segments whose semantic proximity score exceeds 0.8 out of 1.0 are retained. This ensures the agent responds from directly relevant information or acknowledges it does not have the answer. This maintains visitor trust.
05From search to conversation
Semantic search provides content. The AI engine provides form. The result is a conversational, natural response that appears to come from an institution expert. This distinguishes a modern voice AI agent from a simple improved search engine: the search engine gives you a document list; the AI agent gives you an answer.
AI ARTEDUSA uses semantic search so its agent understands your visitors in all the ways they formulate their questions. Find out how at ai.artedusa.com.
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