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Semantic Search

Content managementIntermediate Level

Semantic search is a search method that matches the meaning and intent of a query, not only its exact words, so shoppers find relevant products even with different wording.

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What is Semantic Search?

Semantic search is a search method that matches the meaning and intent of a query instead of only its exact words. A shopper who types 'warm jacket for hiking' also finds products described as insulated outdoor coats. Keyword search looks for the literal terms in titles and descriptions. Semantic search uses language models, often as vector embeddings, to compare meaning, and handles synonyms, typos and natural questions. Many webshops combine both, called hybrid search. The results still depend on your product data: if material, use and size are missing or buried in free text, even a smart search engine cannot match them to the question. AI shopping assistants work the same way, which our guide to AI shopping readiness explains.

Why Semantic Search matters for e-commerce

Shoppers rarely use the exact words in your product titles. They search for a need ('gift for a runner'), a problem or a synonym. Keyword search returns nothing or the wrong items for those queries, and a zero-result page often ends the visit. Semantic search narrows that gap. It also matters beyond your own site: search engines and AI assistants use the same kind of meaning-based matching to decide which products to show.

Examples of Semantic Search

  • 1Query: couch for small apartment, Keyword search: no results, Semantic search: compact two-seat sofas.
  • 2Query: waterproof shoes for walking, Keyword search: only titles with 'waterproof', Semantic search: also hiking boots with a membrane.
  • 3Query: gift for coffee lover, Keyword search: coffee beans only, Semantic search: grinders, cups and brewing gear.
  • 4Query: laptop for video editing, Keyword search: all laptops, Semantic search: models with a strong GPU and lots of RAM.
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How WISEPIM Helps

  • AI enrichment: fill attributes and descriptions that semantic search engines use to match intent.
  • Attribute management: store material, use and size as structured fields instead of free text.
  • AI translation: give search engines in every market descriptions in the local language, across 70+ languages.
  • Quality Guard rules: find products with empty attributes that would be invisible to meaning-based search.

Learn more about Product Enrichment: enrich product data with AI at scale.

Common mistakes with Semantic Search

  • Expecting a semantic search engine to fix thin product data.
  • Dropping keyword search entirely, which hurts exact queries such as SKUs and model numbers.
  • Never reviewing zero-result and low-click queries.
  • Indexing only titles, so attributes and descriptions are ignored.

Tips for Semantic Search

  • Fill the attributes shoppers describe in their own words, such as use, material and fit.
  • Keep exact matching for SKUs, EANs and model numbers.
  • Review your site search logs monthly for zero-result queries.
  • Write descriptions that explain use and benefits, not only specs.

Trends around Semantic Search

  • Hybrid search that combines keyword and vector matching.
  • Conversational search where shoppers ask full questions.
  • AI shopping assistants that pick products for the shopper.
  • Multilingual models that match queries across languages.

Tools for Semantic Search

  • Site search platforms with semantic features, such as Algolia, Elasticsearch and Klevu.
  • Vector databases for custom search setups.
  • Built-in search apps of shop platforms such as Shopify.
  • A PIM system to keep the attributes and descriptions search engines index complete.

Related Terms

Headless CMSProduct Brand Voice GuidelinesVideo Commerce

Also Known As

Meaning-based searchVector searchNatural language search

Frequently Asked Questions

Keyword search matches the literal words in a query to words in your product data. Semantic search compares meaning, so it also finds products described with synonyms or related terms. Most webshops get the best results by combining both.

A language model turns queries and product texts into numeric vectors that capture meaning. The search engine then returns products whose vectors are closest to the query. Filters and ranking rules are usually applied on top.

Search engines such as Google use meaning-based matching themselves. Clear, complete product content that answers what shoppers ask helps both your site search and how search engines understand your pages.

Descriptive titles, full descriptions and structured attributes such as material, use, size and color. The more of that context a product has, the more queries it can match.

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