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 Aug 23, 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-v2. 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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ツールについて
公開記事がこのサイトの意味的中心、トピッククラスター、最も近い編集上の近隣にどれだけ近いかを探索します。
静的なビルド時レポートで中心的なページ、まとまりのあるトピック近傍、編集上の外れ値を確認します。距離を品質スコアとは扱いません。
機能
- 公開記事を対象にしたビルド時分析
- サイト全体の分布とトピック整合性カード
- 検索、トピック絞り込み、並べ替え、CSV出力
- 記事の証拠と内部リンクグラフへの直接導線
仕組み
公開され、人がレビューした記事本文をall-MiniLM-L6-v2で埋め込み、正規化したサイト重心からのコサイン距離をサイト半径にします。決定論的な球面k-meansでトピック重心とトピック内距離を計算し、含まれるコーパスで最も近い記事を示します。
制限事項
- このリリースはPatrickStox.comだけを分析し、クロールやアップロードには対応しません。結果はモデル、コーパス、テキスト処理、タグ、クラスター数に依存します。埋め込み距離は事実、ナビゲーション、商用、事業上の違いを捉えきれず、2ページがリンクすべきかは判断できません。
よくある質問
Semantic Site Mapはどのように計算されますか?
記事の埋め込みをサイト重心からのコサイン距離で測り、決定論的な球面k-meansでトピック重心と距離を計算します。
大きな半径は記事が悪い、またはテーマ外という意味ですか?
いいえ。サイト全体の意味的重心から遠いだけです。全体の中心から離れていても、トピック内では一貫している場合があります。
自分のウェブサイトでSemantic Site Mapを実行できますか?
このリリースではできません。PatrickStox.comのビルド時分析を公開するケーススタディであり、汎用クローラーやアップロード解析ではありません。