Wikipedia’s Entity Map.

30 Seconds.

Wikipedia Entity Correlation Finder

Google sees entities, not paragraphs. Miss the ones your topic requires — Google sees a relevance gap you didn’t know existed.

Type a topic. Every entity Google expects. Ranked by relevance. Ready to export.

Not keyword guesses. The actual semantic relationships.

Wikipedia Link Graph

AI Semantic Embeddings

World First Browser OS

150+ Tools

You’re Writing Blind

You research a topic. You write 2,000 words. You think it’s comprehensive.

Google disagrees.

Which entities does Google consider required for this topic? Which related concepts did you forget? What does “topical authority” actually look like — in practice?

Keyword tools tell you what people search. They don’t tell you what Google expects to find on the page. Google’s helpful content update rewards comprehensiveness. But how do you know what “comprehensive” means for your specific topic?

The Entity Correlation Finder is one of the Local SEO Tools — the rest cover reviews, citations, rankings, and map data.

Four Signals. One Score.

Wikipedia’s knowledge graph combined with AI-powered semantic analysis.

Embedding Similarity → How semantically close is this entity to your topic? The dominant signal that catches relationships text matching misses entirely.
Category Overlap → How many Wikipedia categories do they share? Identifies topical cousins in the same knowledge domain.
Bidirectional Links → Does the entity link TO your topic AND your topic link back? Mutual links confirm the strongest relationships.
Keyword Presence → Does your topic appear in the entity's description? A direct relevance confirmation layer.

Four independent signals. One relevance score you can trust.

LSI Keywords:

Your Unfair Advantage

What Wikipedia editors actually wrote — not what algorithms guessed.

Wikipedia’s internal links use anchor text chosen by subject-matter experts. When an editor writes about “machine learning” and links to “statistical classification,” they might write “statistical methods” or “classification techniques” or “predictive modeling.”

Those anchor text choices are editorial decisions made by 44 million human contributors who understand the topic deeply enough to explain it to other humans.

Every other LSI tool reverse-engineers what might be relevant. Entity Correlation reads what Wikipedia contributors already decided IS relevant. One is a guess. The other is a citation.

Topical Authority

Made Measurable

See the entity gap before you publish.

You write about “email marketing.” Your competitor ranks higher. Why?

Their content mentions: marketing automation, lead nurturing, A/B testing, open rates, click-through rates, list segmentation, GDPR compliance, double opt-in, transactional email, drip campaigns.

Yours mentions: email marketing, newsletters, subscribers.

Google sees a comprehensiveness gap. You don’t — because you didn’t know what was missing. Entity Correlation shows you the gap before you publish.