Knowledge graphs: how AI connects your business to everything else
Entities, not keywords
Older search systems mostly matched keywords: a page containing the words "plumber" and "Barrie" ranked for that query. A knowledge graph reasons in terms of entities and relationships instead — it needs to know that a specific business is a plumber, is located in Barrie, and is connected to a set of reviews and a phone number, as a single coherent record, not just a page with matching words on it. This is a meaningfully different kind of understanding, and it's the reason two businesses with similarly keyword-optimized pages can get very different AI treatment if only one resolves cleanly as an entity.
What breaks entity resolution
- Inconsistent naming. "Acme Plumbing Co.," "Acme Plumbing," and "Acme Plumbing & Heating Inc." across different listings look like three different, weaker entities instead of one strong one.
- Mismatched location data. A different address or phone number on your website than on your Google Business Profile creates ambiguity about which is authoritative.
- No structured signal at all. A site with no schema markup and only loosely-worded prose forces the AI to infer the entity from context, which is slower and less reliable than being told directly.
- Category ambiguity. A business that describes itself differently in different places ("home services," "plumbing," "plumbing and HVAC") makes it harder for the graph to place it confidently in the one category a buyer is actually asking about.
How to strengthen your entity signals
- Pick one name, address, and phone number and use it identically everywhere — your site, directories, social profiles, review platforms.
- Add Organization or LocalBusiness schema with your category, location and contact details stated explicitly, not just implied by prose.
- Keep your category consistent across every listing, rather than letting each platform's own default wording drift.
- Link your properties together — your site linking to your verified social and review profiles, and vice versa, reinforces that they all describe the same entity.
Frequently asked questions
What is a knowledge graph?
A knowledge graph is a structured database of entities (people, places, organizations, products) and the relationships between them — for example, a business connected to its industry category, its city, its owner, and its reviews. AI systems use knowledge graphs to reason about how things relate, not just to store isolated facts.
What is an entity in this context?
An entity is a distinct, identifiable thing a knowledge graph can represent — a specific business, a person, a product, or a location — as opposed to a generic keyword. A business becomes a clear entity when its name, category, location and identity are consistent and unambiguous across the web.
How does this affect whether AI recommends my business?
If an AI system can't confidently resolve your business into a single, well-connected entity, it can't reliably connect you to the category someone is asking about. Inconsistent business names, mismatched addresses across directories, or a lack of any structured presence all weaken entity resolution and reduce the odds of being recommended.
How do I strengthen my entity signals?
Use one consistent business name, address and phone number everywhere you're listed (directories, your own site, social profiles), add Organization or LocalBusiness schema markup, and make sure your category and services are stated clearly and consistently across all of it.
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