Map the entities behind any page
Paste a page, a competitor's article or an AI answer and see it the way a search engine might: as a network of entities. The tool finds the central entity, the topical clusters, and the gateway concepts that connect them, then turns the result into Wikidata-linked schema markup.
Add the content you want to map
How to use it
- 1
Add content
Paste a full article, or pull a Wikipedia article to see the knowledge-base view of a topic. Headings on their own lines give the cleanest result.
- 2
Read the clusters
Each colour is a context the text covers, and its share shows how much of the text it takes up. A single dominant colour means a narrow page. Many disconnected colours means a page doing several jobs.
- 3
Use the gateways
Gateway concepts connect clusters. They make the best anchor text for internal links and the best hinge sentences between sections.
- 4
Fix the structural gaps
Large clusters that never touch are either a missing explanation or a reason to split the page. Each gap comes with a bridge question.
- 5
Ship the schema
Link the top entities to Wikidata, reject wrong matches, and copy the about/mentions JSON-LD into your page.
What is measured, and what is framework
Semantic SEO mixes real information-retrieval science with practitioner frameworks. Each output here is labelled so you know which one you're looking at.
Lemmatised terms plus multi-word phrases that co-occur more often than chance (collocations), and capitalised named-entity sequences. Stop words and generic verbs are removed.
Co-occurrence within a sliding window (default four words) that never crosses a sentence. Closer words get more weight. This is the same text-network approach used by InfraNodus and in academic discourse analysis.
Weighted betweenness centrality (Brandes) ranks each term's influence. Louvain modularity finds the topical clusters. Gateways have high betweenness and a high participation coefficient across clusters.
These ideas come from Koray Tuğberk Gübür's semantic SEO framework. They are useful for planning content, but they are not confirmed ranking factors.
This is the top search match, not true disambiguation. Always check the QIDs before you publish the schema, and click “wrong?” to drop a bad match.
Questions about the Entity Graph Mapper
What is entity-driven SEO?+
Entity-driven SEO organises content around things (people, products, concepts) and how they relate, rather than around isolated keywords. Search engines and LLMs represent topics as connected entities, so pages that clearly define their entities and connect them tend to be easier to understand and retrieve.
How is this different from a keyword density tool?+
Density counts words. This tool maps relationships. A term mentioned only twice can still be one of the most influential concepts on a page if it connects two otherwise separate topics. Betweenness centrality captures that, and frequency doesn't.
What should I do with conceptual gateways?+
Use them as anchor text when linking between pages in different clusters, and as transition sentences between sections. They are the concepts that already connect your contexts.
Does adding about/mentions schema improve rankings?+
There is no public evidence that it is a direct ranking factor. What it does is state explicitly, with Wikidata identifiers, which entities the page is about. That helps disambiguation for search engines and AI systems at almost no cost.
Is my content stored anywhere?+
No. All analysis runs in your browser. The only network calls are to Wikipedia and Wikidata, and only when you click those buttons. They send the term or topic, not your text.
Content Gap Analyzer
Compare your page against the ranking pages and real search demand, then get a content brief built from the gaps.
Done-for-youWant a full topical map built for you?
I build entity-driven topical maps, content briefs and internal-linking plans for service, SaaS and eCommerce sites. Message me on WhatsApp.