Semantic SEO Architecture

Discipline: Information Retrieval | Focus: Topic Modeling & Vector Proximity

1. Definition: From Strings to Vectors

Semantic SEO is the discipline of structuring unstructured data into a Knowledge Graph. Traditional SEO focuses on "Keywords" (Strings). Semantic SEO focuses on "Meanings" (Vectors). It reduces the cosine distance between related nodes in a vector space, signaling to the engine that a cluster of pages covers a topic exhaustively.

2. Topological Structure (The Hub & Spoke Model)

To maximize SheepRank (Authority Flow), we abandon linear site structures for Hub & Spoke Topologies. This concentrates semantic density.

[Pillar Page / Hub] (High Authority Node) │ ┌───────────┼───────────┐ │ │ │ [Cluster A] [Cluster B] [Cluster C] (Specific) (Specific) (Specific) │ │ │ └───────────┴───────────┘ (Internal Link Mesh: "RelatedTo")

The Logic: The Pillar Page defines the Entity. The Cluster Pages define the Attributes. The internal links act as the edges of the graph, transferring PageRank and Context.

3. Implementation: The Siloing Protocol

There are two methods to enforce semantic clustering. We prioritize Virtual Siloing for flexibility.

A. Physical Siloing (Directory Based)

URL structure dictates the graph. Rigid but clear.

https://domain.com/seo/semantic/definition/
https://domain.com/seo/semantic/examples/
    

B. Virtual Siloing (Link Based)

URL structure is flat, but internal linking is strict. This is preferred for AI SEO as it allows an entity to belong to multiple clusters dynamically.

4. Engineering The "Breadcrumb" Logic

Semantic structure must be declared in the DOM, not just implied. We use BreadcrumbList Schema to force the hierarchy into the Search Engine's understanding, even if the URL structure is flat.

// JSON-LD: Forcing Hierarchy via Code
{
  "@context": "https://schema.org",
  "@type": "BreadcrumbList",
  "itemListElement": [
    { 
      "@type": "ListItem", 
      "position": 1, 
      "name": "Home", 
      "item": "https://eyalkrief.fr/" 
    },
    { 
      "@type": "ListItem", 
      "position": 2, 
      "name": "Methodology", 
      "item": "https://eyalkrief.fr/blacksheep-seo/" 
    },
    { 
      "@type": "ListItem", 
      "position": 3, 
      "name": "Semantic Architecture", 
      "item": "https://eyalkrief.fr/semantic-seo/" 
    }
  ]
}
    

5. Measuring Semantic Density (Python Logic)

How do we know if a cluster is "dense" enough? We analyze Term Frequency-Inverse Document Frequency (TF-IDF) across the cluster. Below is the logic used by our SNTNL Engine.

# Pseudo-code for Semantic Density Calculation
def calculate_density(cluster_pages, target_entity):
    corpus = [page.content for page in cluster_pages]
    vectorizer = TfidfVectorizer()
    vectors = vectorizer.fit_transform(corpus)
    
    # Check if the Target Entity is central to all vectors
    centrality = cosine_similarity(vectors, target_entity.vector)
    
    if centrality < 0.7:
        return "Diluted Cluster: Strengthen Internal Linking"
    return "Dense Cluster: Authority Established"
    

6. Anti-Patterns (Common Mistakes)

⚠️ The "Orphan" Fallacy: Never create a page that is not linked from a parent Pillar. An orphan node has a PageRank of 0 and cannot be ingested effectively by the graph crawler.

⚠️ The "Cannibalization" Risk: Creating two pages for the same intent splits the vector score. Always consolidate or canonicalize overlapping entities.