Semantic Keyword Research

Semantic keyword research is an approach to keyword research that looks at what searches mean, not only at which words they contain. It asks why people search, what content they expect, how their searches connect into journeys and what they already know. The output is a structured keyword universe organised around entities, search intent and topics that a whole organisation can act on.

How it works

Traditional keyword research collects terms, attaches search volume and keyword difficulty and picks targets one by one. MLforSEO’s course argues this is no longer enough, because modern search systems interpret meaning, expand queries and personalise results. A semantic workflow adds layers on top of the raw keyword list:

• Entities and the EAV model: identify the entities in your space, their attributes and the variables of each attribute (dog food, then food type, then kibble, canned or raw). See Entity Attribute Variable (EAV Model).
• Knowledge graph relationships: map how entities connect, using sources such as the Knowledge Graph Search API.
• Intent and SERP analysis: classify intent and read SERP features to see which formats Google expects.
• Context and behaviour: query sequences and paths, session context and implicit user feedback.
• Information gain: what you can add that existing results do not cover.
• ML at scale: entity extraction, clustering, embeddings and text classification to label thousands of keywords consistently.

How it’s used in SEO and marketing

• Content strategy and briefs built on topics and entities rather than single phrases.
• Site architecture and keyword mapping based on intent and topic clusters.
• Preparing for AI search, where query fan-out means a page is retrieved for many sub-questions it was never explicitly targeted at.
• Sharing one categorised dataset with product, UX and paid teams, so the research informs more than blog posts.

Example

A pet food brand starts with 8,000 keywords. Entity extraction finds the main entities (breeds, food types, health conditions), the EAV model turns them into attribute and variable combinations, SERP data marks which combinations want comparison pages versus guides, and sBERT maps each keyword to a topic. The result is a prioritised universe where a gap such as “grain-free food for senior dogs with allergies” becomes a planned page with a clear intent.

Limitations and things to watch

• It takes more data work than a volume-sorted list, and the value only appears if the output is categorised so others can use it.
• ML labels (entities, intent, clusters) need human checks before they drive decisions.
• Volumes are still useful; semantic research adds context to them rather than replacing them.

Learn it on MLforSEO

• Semantic ML-enabled Keyword Research
• How to Turn Semantic Keyword Research Into an Actionable SEO Workflow
• What Does a Good Semantic Keyword Universe Actually Look Like?
• What Is the EAV Model in SEO?
• Transitioning from Traditional to Semantic Keyword Universe (Checklist)