Entities, Entity Attributes, Entity Attribute Variables (EAV Model) – Lesson Preview
Modern search has shifted from strings to things. This lesson shows Marketing and SEO Professionals how to move beyond keyword lists and think in entities the real-world people, places, products, and ideas users mean when they type a query. You’ll learn why Google’s systems interpret queries through entities and relationships, and how the EAV (Entity–Attribute–Variable) model helps you mirror that understanding in your research, site architecture, and content briefs.
We unpack entities vs. keywords with examples, then apply the EAV model to uncover scalable, high-intent patterns (single and multi-variant) you can turn into templates or programmatic pages, without spamming. You’ll see how to extract entities from your keyword universe, identify co-occurring term patterns, and prioritize combinations by business purpose and brand relevance, even when search volume is negligible.
The result: content that aligns with user journeys, fills information gaps, and earns visibility.
What you’ll learn (why it matters)
- Separate entities from keywords because intent lives in concepts, not strings.
- Apply the EAV model because attributes/variables reveal user-valued angles.
- Extract and cluster entities because patterns guide briefs and templates.
- Prioritize by business relevance because low volume can drive revenue.
- Design single/multi-variant patterns because they scale without duplication.
- Validate before publishing because quality and differentiation win.
Key concepts (with mini-definitions)
- Entity — a distinct real-world concept (person, place, thing, idea).
- Keyword vs. Entity — words typed vs. the concept intended by the user.
- EAV Model — entity + its attributes + the attribute’s specific values.
- Entity Attribute — a property describing an entity (e.g., dog food type).
- Entity Attribute Variable — a concrete value of an attribute (e.g., kibble).
- Pattern Keywords — recurring n-gram + entity structures that signal intent.
- Knowledge Graph — connections among entities powering contextual results.
- Single vs. Multi-variant — one attribute vs. combined attributes in a query.
Tools mentioned
Google Natural Language API, MLforSEO Entity Extraction Template (Google Sheets), KeyBERT, N-grams custom formula, SEMrush, DataForSEO, Python and Google Sheets.
Practice & readings
- Use the MLforSEO Google Sheets template to extract entities from your keyword list.
- Run KeyBERT/N-grams to surface pattern keywords and cluster candidates.
- Review the MLforSEO blog tutorials/checklist referenced in the lesson.
Key insights & takeaways
- Search volume is a poor proxy; business purpose and brand fit matter more.
- Blend SEO importance with entity semantics for intent-aligned content.
- EAV reveals info gaps you can fill to differentiate.
- Programmatic opportunities require strict validation and quality control.
- Prioritize combinations you can serve with real user value.
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Turn scattered keywords into an entity-driven strategy you can prove with better alignment and clearer briefs.