What Semantic SEO Is
Semantic SEO treats search engines as information retrieval (IR) systems that match documents to queries based on meaning, not just keyword presence. A semantic SEO strategy builds content that satisfies the IR system's document-relevance model — which Google's algorithm implements through neural embeddings, entity graphs, and distributional semantics.
Koray Tugberk Gubur's methodology is the most systematized publicly available semantic SEO framework. It synthesizes IR theory, search patent analysis, and empirical testing into a reproducible process.
The Three Dimensions of Topical Authority
Koray's model defines topical authority across three dimensions that must all be satisfied:
Dimension 1: Vastness
Definition: The total number of unique queries and sub-topics your content covers within a topical domain.
Vastness is the breadth dimension. A site with 50 pages on roofing covering every sub-topic (materials, costs, installation processes, permits, maintenance, storm damage, insurance, regional differences) has higher topical vastness than a site with 10 pages covering only the highest-volume queries.
Why vastness matters for IR systems: Search engines build topical models by indexing content across all facets of a topic. A site that covers more facets gets recognized as a comprehensive source — which boosts authority for all queries within the topic, including queries the site hasn't directly targeted.
Vastness building tactic: Use the topical map to identify every query cluster within your domain. Calculate coverage gaps: topics where competitors have 3+ pages and you have zero.
Dimension 2: Depth
Definition: The contextual completeness and expert-level coverage within each piece of content.
Depth is the vertical dimension per document. A piece on "roof replacement cost" that mentions the price range and moves on has low depth. A piece that covers: cost by material type, cost by region, how labor costs vary, what drives variation in estimates, what the permit cost range is, and what affects the 10-year cost-of-ownership — has high depth.
Why depth matters: IR systems evaluate whether a document satisfies a query completely. Partial answers signal low authority. Complete answers signal the document is the definitive source — which improves ranking for all semantic variants of the query.
Depth building tactic: For every major topic page, identify the PAA questions and common search modifiers for the query. Each modifier represents a contextual dimension the reader may want covered. Complete depth means addressing all dimensions without the reader needing to navigate away.
Dimension 3: Momentum
Definition: The recency and velocity of content publication within a topical domain.
Momentum is the temporal dimension. A site that published 50 roofing pages 3 years ago and has published nothing since has zero momentum. A site that publishes 2-3 new pages per week within the roofing topical domain has high momentum.
Why momentum matters: Google's QDF (Query Deserves Freshness) algorithm and its recency signals reward sites that actively contribute new information. More importantly, content publication momentum signals to Google that this domain is an active authority — maintained and expanding — vs. a historical authority that may be decaying.
Momentum building tactic: Content calendar with minimum 1-2 new topically-relevant pages per week. Even 300-word PAA posts count. The publication frequency signal matters as much as the content quality.
The COREIS Model
Koray's COREIS model defines the five components a topical content architecture must satisfy:
| Component | Meaning | Implementation | |-----------|---------|----------------| | Context | Every page has a defined contextual position within the topic map | Topical map with clear hub-spoke relationships | | Order | Content is organized logically within the reader's learning journey | Pillar → sub-topic → long-tail publishing order | | Relevance | Internal links connect contextually related content (not random) | Semantic similarity drives linking decisions | | Entity | Business and topic entities explicitly declared and connected | Schema + in-text entity declarations | | Intent | Every page satisfies a specific search intent type | Intent classification before content creation | | Structure | Heading hierarchy, content format, and schema reflect the content type | H1→H2→H3 semantic hierarchy + matching schema |
How This Differs from Traditional SEO
Traditional SEO: Target high-volume keywords, build pages for those keywords, get links to those pages.
Semantic SEO: Build a complete information architecture for a topical domain, satisfy all IR system relevance signals across the entire topic, earn recognition as the domain authority.
The ranking outcome is similar but the path is different. Traditional SEO optimizes for individual query competition. Semantic SEO optimizes for domain-level topic ownership — which produces ranking results across the entire query family, including long-tail queries the site never explicitly targeted.
Related Koray Skill Modules
The Koray framework is distributed across several execution skills:
koray-topical-map— Vastness architecture (building the topical coverage map)koray-contextual-structure— page-level Depth implementation (main vs supplementary content, contextual hierarchy)koray-contextual-vector— heading construction that implements the IR model at document levelkoray-microsemantics— Depth tactics at the sentence level (predicate+noun optimization)koray-query-semantics— Intent dimension implementation (canonical vs represented queries)koray-content-brief— COREIS model in executable brief formatkoray-content-audit— evaluating existing content against all three dimensions
Cross-References
koray-topical-map— Vastness dimension executionkoray-contextual-structure— Depth dimension at page leveltopic-cluster— traditional equivalent of topical map for simpler deploymentsentity-clouds-seo— Entity component of COREIS model
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