Semantic Site Map
Free, no signup. Explore how 0 published articles are distributed around this site's semantic center. Find central pages, coherent topic neighborhoods, and editorial outliers in this static, build-time report without treating distance as a quality score.
Current build-time report · eligible published corpus
Report generated Jul 27, 2026.
This is a precomputed public report — filters and sorting run in your browser, and nothing you enter is sent or stored. Anonymous run-level outcome counters may be used for aggregate research; URLs, domains, IPs, and identifiers are never included, and no statistic is released below 100 runs.
Site-radius distribution
Each dot is an article. Select one to open its article-level AI + SEO Data view.
The horizontal position is relative to this report's observed minimum and maximum; it is not a universal 0–100 grade.
Topic coherence
Lower mean distance means the articles in that model-created cluster sit closer together.
Topic coherence needs enough eligible articles to form more than one meaningful cluster. No topic comparison is available in this build.
Article evidence
Search, filter, and sort the complete included set.
Showing 0 articles
No articles match this search and topic filter. Clear the search or choose All topics.
| Article | Site radius | Topic | Topic radius | Closest articles |
|---|
How to use it
- Start with the distribution to see the site's overall semantic spread.
- Compare topic mean and maximum radii to find tight and broad editorial neighborhoods.
- Filter the evidence table by topic, then inspect the most central and furthest pages.
- Open an article to see its nearest semantic neighbors and article-level context.
- Compare semantic structure with the Internal Link Cluster Visualizer; semantic similarity and actual internal links answer different questions.
What the results mean
- Site radius is cosine distance from the centroid of every included article embedding, scaled for readability.
- Centrality rank orders the included articles from the smallest site radius to the largest.
- Topic radius is an article's distance from its assigned topic centroid.
- Topic mean/max summarizes how tightly the topic's member embeddings group together.
- Closest articles are the three highest cosine-similarity neighbors in the included corpus.
Features
- Build-time analysis of every eligible published article.
- Site-wide distribution, topic coherence cards, and complete evidence table.
- Search, topic filtering, sorting, and CSV export.
- Direct handoff to article evidence and the link-graph visualizer.
Limitations
This release analyzes PatrickStox.com only; it does not crawl or accept uploads. Results depend on the model, corpus, text cleanup, tags, and cluster count. Embedding similarity can miss factual, navigational, commercial, and business distinctions, and it cannot tell whether two pages should link.
Frequently asked questions
Can I run Semantic Site Map on my own website here?
Not in this release. This public explorer exposes the build-time analysis for PatrickStox.com. It is a transparent case study, not a general-purpose crawler or upload analyzer.
Does a larger radius mean an article is bad or off-topic?
No. It means the article is farther from the site-wide semantic centroid. A page can be far from the global center while fitting tightly inside a coherent topic cluster.
How is Semantic Site Map calculated?
Published, human-reviewed article text is embedded with all-MiniLM-L6-v2Embeddings are dense numerical vectors — lists of floating-point numbers — that represent the meaning of text in a high-dimensional space. Semantically similar content lands close together, so search and AI systems can match by meaning, not just keywords.. Cosine distance from the normalized site centroid becomes the site radius. Deterministic spherical k-means supplies topic centroids and within-topic distance.
Why are some articles excluded?
Preview, draft, example, and AI-labeled content is excluded. The report covers only published editorial articles that count toward site-wide aggregates.
Is Semantic Site Map a Google ranking signal?
No. It is a site-planning diagnostic built from one embedding model. Google does not provide or endorse this metric.
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Common issues & how to fix them
- Needs review Eligible corpus is too small for comparison Fix: Publish or approve enough eligible human-reviewed articles for a meaningful comparison, then rebuild the report.
- Warnings Article is far from the corpus topic center Fix: Review whether the article intentionally expands the site’s scope; connect it to the relevant topic or revise its focus if not.
- Warnings Article is distant from its nearest content cluster Fix: Review the article against its assigned cluster and clarify its topic or cluster placement when the distance is unintended.
- Information Corpus is split across weakly connected topic groups Fix: Create deliberate editorial bridges between genuinely related topic groups instead of forcing unrelated clusters together.
- Warnings Corpus has low topical coherence Fix: Review the broadest clusters and split or refocus pages only where the model’s evidence matches editorial intent.