Key Engineering Takeaways
- Search engines evaluate entities and their relationships, not just ungrounded keywords.
- Structuring content into explicit triplets (Subject -> Predicate -> Object) powers clear extraction by LLMs and Google Knowledge Graph.
- EAV modeling prevents internal content cannibalization by maintaining strict entity scopes across pillar-cluster networks.
Use EAV as a planning model, not keyword decoration
An Entity–Attribute–Value model forces a page to state what thing it covers, which property is being discussed, and which value answers the question. It is useful for defining page scope, spotting missing attributes, and preventing two URLs from competing for the same semantic job.
From entity inventory to page architecture
- Name the central entity and the search task the page must satisfy.
- List attributes that are relevant, contextual, and expected by the audience.
- Separate values that belong on the same page from those that deserve distinct page roles.
- Express stable relationships in visible copy and matching structured data.
- Use internal links to connect parent entities, subtypes, comparisons, and evidence.
Example triple set
page_scope.json
[
{"entity":"SEO audit","attribute":"input","value":"crawl + GSC data"},
{"entity":"SEO audit","attribute":"output","value":"prioritized findings"},
{"entity":"SEO audit","attribute":"verification","value":"live change check"}
]The boundary that matters
Schema cannot rescue vague content. The visible page, internal-link context, metadata, and JSON-LD should describe the same entity and the same role.
Core Disciplines
Entity-Attribute-Value (EAV)Semantic SEOKnowledge GraphsTopical AuthorityStructured Data