What is semantic enrichment in ecommerce?

In libraries, archives or language processing, semantic enrichment means annotating a text with the entities it names. In an online store it is the same thing applied to product data: it is not about adding data, but about saying what each piece of it means. That is why it is not another name for catalog enrichment, which fills in and corrects the values of each field. A field can be full and still be ambiguous: "Capacity: 30" does not say whether it is liters or kilos, and "Category: New arrivals" does not say what the product is. It matters to you because anyone reading your catalog from outside, whether Google or an AI assistant, has to interpret each value, and what is not said gets guessed. Google puts it this way: you can help it understand a page by giving it explicit clues about its meaning with structured data.⁠^{[1]Google Search Central. Introduction to structured data markup in Google Search}

How it works

Semantic enrichment is done with four operations, from the most general to the most specific.

1. Say what kind of thing it is. An HTML tag tells the browser how to display a piece of text, but not what it means. Schema.org's example is a heading that says "Avatar": it could be the movie or a profile picture, and the code cannot tell them apart.⁠^{[2]Schema.org. Getting started with schema.org using Microdata} Declaring that a page describes a Product, that its maker is a Brand and that whoever sells it is an Organization turns a piece of text into a typed entity. Google uses the structured data it finds to understand the content of the page and to gather information about the world, such as the people, books or companies that appear in the markup.⁠^{[1]Google Search Central. Introduction to structured data markup in Google Search}

2. Use shared properties. Instead of a label of your own like "Col." or "Fabric," the properties the vocabulary defines: color, material, gtin. For what has no property of its own, schema.org offers additionalProperty with a value of type PropertyValue, which accepts a propertyID, the usual identifier for that characteristic, such as the code from a product-properties standard, and a unitCode, the unit according to the UN/CEFACT common code, three characters long, or a URL.⁠^{[3]Schema.org. PropertyValue}

3. Normalize the values. Some data a machine cannot interpret without a convention. Schema.org uses a date as its example: "04/01/11" could be January 11, 2004, April 1, 2011 or January 4, 2011, and the fix is to write it in ISO 8601 format. The same goes for closed lists, like availability, which is expressed with fixed values such as https://schema.org/InStock and not with an "only a few left."⁠^{[2]Schema.org. Getting started with schema.org using Microdata} A number without a unit, a date without a format and a status in free language are the three most common semantic gaps.

4. Link with stable identifiers. The GTIN identifies the product outside your store; sameAs links the brand to a reference page about it, and schema.org explains that links to third-party sites about the same item help search engines better understand what you are describing.⁠^{[2]Schema.org. Getting started with schema.org using Microdata} Category works the same way: Google Merchant Center accepts Google's category as a full path or as a numeric ID, and recommends the ID.⁠^{[4]Google Merchant Center Help. Product data specification} In Google's taxonomy, "Luggage & Bags > Backpacks" is category 100.⁠^{[5]Google. Google Product Taxonomy (taxonomy-with-ids.en-US.txt)} It is someone else's product taxonomy, which is why whoever reads it understands it.

If you don't do it, the platform does

Shopify Catalog, the product database Shopify uses to supply AI channels, does part of this work on its own. In December 2025, Shopify explained that the catalog infers categories, extracts attributes, consolidates variants and groups identical items so AI can understand the data.⁠^{[6]Shopify Newsroom. Introducing Shopify Agentic Storefronts: Sell your products everywhere AI conversations happen} Its developer documentation adds the fine print: some fields are inferred by Shopify's AI, may be missing or of varying accuracy depending on the available product data, and are marked as inferred.⁠^{[7]Shopify (shopify.dev). About Catalogs} The lesson is not about Shopify: what the store does not make explicit, someone infers, and the store stops deciding what its data means.

In a store

Fictional example. A mountain gear store sells a 30-liter backpack. Its product page has every field filled in, and yet almost all of them are ambiguous to a machine:

In JSON-LD, the right-hand column looks like this:

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Trailhead 30 Backpack",
  "brand": {
    "@type": "Brand",
    "name": "Trailhead",
    "sameAs": "https://trailhead.example/"
  },
  "sku": "TRAIL30-BLK",
  "additionalProperty": {
    "@type": "PropertyValue",
    "name": "Capacity",
    "value": 30,
    "unitText": "liters"
  },
  "offers": {
    "@type": "Offer",
    "availability": "https://schema.org/InStock"
  }
}

The text on the product page has not changed; what has changed is what a machine can state about it without guessing. Clione, for example, publishes a single @graph on each product page whose product node carries a stable @id and coexists with the theme's instead of duplicating it.

How to check it

1. Run a product page URL through the schema validator and see which types it detects. If only a web page shows up, the product is not declared as such.
2. Look for numbers without units in your attributes: capacity, weight, dimensions, power. Each one needs its unit in a field, not in the product name.⁠^{[3]Schema.org. PropertyValue}
3. Look at how you send the category to Merchant Center. If you send your collection's name instead of a category from Google's taxonomy, swap it for the ID.⁠^{[4]Google Merchant Center Help. Product data specification}⁠^{[5]Google. Google Product Taxonomy (taxonomy-with-ids.en-US.txt)}
4. Review availability in the markup: it has to be one of schema.org's fixed values, not text.⁠^{[2]Schema.org. Getting started with schema.org using Microdata}
5. Check the brand: if it is only text, declare it as an entity and, if the brand has a reference page, link it with sameAs.

Related concept: catalog enrichment

The two jobs are done on the same product catalog and often go together, but they answer different questions. Catalog enrichment answers what each field is worth: it fills in the missing material, fixes a GTIN or writes a description. Semantic enrichment answers what that value means to a machine: that the material is the material property of a Product, that the 30 is liters, that the category is Google's 100. A catalog can be complete and semantically poor, but not the other way around: you cannot declare the meaning of a value that does not exist.

To go further, the catalog and entities reading path orders the rest of the terms, up to the product graph, which brings all these entities together in a single piece of markup.