July 21, 2026 • Search Engine Land
Schema markup should be treated as a knowledge graph build rather than a rich results tactic, according to a framework published by Search Engine Land. The argument is that structured data’s real value in AI search is describing the entities a business covers and how they relate to each other — which in turn makes it possible to find and rank the gaps where AI systems have nothing of yours to work with.
Key takeaways
- Schema markup can be used to build a site-level knowledge graph defining entities and the relationships between them, not just to qualify for rich results.
- A custom schema framework lets a site assess its own entity coverage and rank the gaps by priority, turning structured data into a content strategy input.
- Vector embeddings can analyze the semantic context of a site to check whether relevant entities are genuinely covered.
- The output is a prioritized list of missing entities rather than a keyword list.
The argument: schema is underused
Most implementations stop at eligibility. A business marks up its organization, a few products, an FAQ block, confirms the rich result appears, and considers the job finished. The framework treats that as leaving most of the value on the table. Schema is a machine-readable description of what a business is, what it does, and how those things connect — and AI search systems consume exactly that kind of description.
The distinction matters because rich results are a rendering decision made by a search engine. Entity description is an input that any machine reader can use, including ones that never render a SERP feature at all.
Building the graph
Rather than isolated markup blocks, the approach connects entities into a graph. Services, locations, people, products, and topics are each defined and then linked, so a machine reading the site can follow the relationships instead of inferring them from prose. The site stops being a set of pages that happen to carry structured data and becomes a described set of things with stated connections.
Finding and measuring the gaps
Once entities are defined, coverage becomes measurable. A custom schema framework can assess which entities in a subject area the site actually describes and which are absent. Those absences are the entity gaps: topics where an AI system fielding a question has no material from the site to draw on.
The framework also applies vector embeddings to analyze a site’s semantic context. Comparing what the site covers against the broader topic space surfaces gaps a manual audit tends to miss, because it compares meaning rather than matching strings. The gaps are then ranked, so content work targets the entities that matter most instead of whatever a keyword tool happened to surface — which reorders the usual planning sequence to entity coverage first, keywords second.
What it means for small businesses
The full framework — custom schema tooling plus embedding analysis — is built for sites with scale. The underlying diagnosis applies at any size, and for a London, Ontario service business the entity gaps are usually not subtle. A service that gets performed weekly but has no page. A town that gets driven to but never named. An owner with twenty years in the trade who appears nowhere as an identified person. These are entities the business owns and has simply never described in a form a machine can read.
That is technical SEO work before it is content work. A business can only mark up entities that exist somewhere on the site, so the audit produces two lists: things to describe properly in schema markup, and things that need a page before markup is even possible. Doing it in the other order — commissioning content first, marking it up later — is how sites end up with coverage that looks broad in a content calendar and reads as thin to a machine.
The same logic extends to data held off-site. A Google Business Profile is entity data about the business held in a structured format, and its categories, service areas, and attributes should agree with what the site’s markup says. Contradictions between the two are their own kind of gap.
The ONmetrics Take
The useful reframe here is that schema stopped being a formatting trick. For years the honest answer to “why add structured data” was “you might get stars, a price, or an FAQ dropdown.” That answer made schema a nice-to-have that got cut whenever a budget got tight. Describing your entities so machines can reason about them is a different proposition, and it does not depend on Google choosing to render anything.
For most small businesses the practical version is smaller than the framework it came from. Do not start with embeddings. Start by listing what you actually are: every service you sell, every area you serve, every person a customer would want vetted, every product line. Then check which of those exist as a described entity rather than a phrase buried in a paragraph. The gaps tend to be embarrassing and quick to close.
The failure mode to avoid is marking up entities that do not hold up — a service with no page behind it, a service area you do not really cover. Structured data makes claims legible and therefore checkable. Overstating in markup is worse than staying quiet.
Get a free digital marketing audit and we will map what your site currently describes to a machine, and what it leaves for one to guess at.
Source
Original reporting: Search Engine Land — “Schema for AI search: How to identify and prioritize entity gaps.” https://searchengineland.com/schema-ai-search-identify-prioritize-entity-gaps-482728