Knowledge Graph SEO: How Google Understands Brands & Entities in 2026

How Google’s Knowledge Graph Actually Works in 2026

Most practitioners treat the Knowledge Graph as a black box — something Google manages internally that you can influence but never fully understand. That framing leads to passive optimisation strategies that produce inconsistent results.

The Knowledge Graph has a documented structure. Understanding it changes how you prioritise your efforts.

What the Knowledge Graph Stores and How It Differs From the Search Index

Two separate systems operate beneath every search result — the index and the Knowledge Graph. The index stores documents and their signals — pages, links, crawl data, content quality indicators. The Knowledge Graph stores entities and their relationships — people, organisations, places, concepts, and the connections between them.

When you publish a post about “cloud security,” Google’s index processes the document. When Google identifies “your brand” as an entity associated with “cloud security,” it writes a relationship to the Knowledge Graph. These are different systems with different signals.

Optimising only for the index — content quality, keyword signals, backlinks — doesn’t build entity relationships in the Knowledge Graph. Both need work.

How Google Builds Entity Associations From Co-occurrence, Schema, and Citations

Three primary mechanisms drive entity association building in Google’s Knowledge Graph:

Co-occurrence: Your brand name and a topic name appearing together in crawled content — on your site, on third-party sites, in news coverage, in academic citations. The more reliably these co-occur in topically relevant contexts, the stronger the association signal.

Schema markup: Organisation schema with a verified sameAs array pointing to authoritative linked data sources (Wikidata, LinkedIn, Companies House, Crunchbase) signals to Google’s entity resolution system that these are verified references to the same entity.

Citations: Third-party mentions that include your brand in a topically relevant context — editorial citations, research citations, AI system citations (Perplexity, ChatGPT). Each citation that names your brand alongside a specific topic strengthens the association signal (Google, Knowledge Graph Search API documentation, 2024). None of these signals work in isolation. All three compound.

The Three Signals Google Uses to Determine Topical Association Strength

Observed patterns indicate Google weights topical association across three dimensions:

Consistency: How reliably does your brand appear alongside this topic across different sources and contexts? Inconsistent signals — your brand associated with ten different topics at low frequency — produce weak salience on all of them.

Authority of association source: A citation from a Wikipedia article, a Google Scholar paper, or a high-DR editorial publication carries more entity association weight than a citation from a directory listing or a low-authority blog.

Recency of association signals: Entity associations that were strong three years ago but haven’t been reinforced with recent signals decay. Google’s Knowledge Graph is not a permanent record — it’s a living model that updates from new crawl data.

Build reliable signals. Prioritise authoritative citation sources. Maintain signal frequency over time.

The Entity Salience Stack: The Four-Layer Authority Model

The Entity Salience Stack is the model this pillar introduces for building brand entity salience systematically across four compounding layers. Most Knowledge Graph SEO guides address one or two of these layers in isolation. The stack shows how they build on each other — and why skipping a layer produces ceiling effects that content quality alone can’t overcome.

The part most guides skip is that the stack is sequential — not modular. You can’t activate Layer 3 verification signals before Layer 1 declaration is resolved.

The stack does not replace content quality or traditional SEO signals. It sits alongside them. A brand with excellent content but a weak Layer 1 (declaration) is building topical authority on an entity foundation Google can’t verify.

Layer 1 — Entity Declaration (Schema + Wikidata + sameAs Signals)

Declaration is the foundation. Before Google can build entity associations, it needs to resolve your brand as a distinct, verifiable entity — separate from other organisations with similar names, separate from individuals associated with your brand, separate from your products.

Three declaration signals matter:

Wikidata entity entry: A verifiable, machine-readable entity record that Google’s Knowledge Graph directly reads. Wikidata entries include instance of (Organisation), official website, country, and relationship fields that Google uses for entity resolution.

Organisation schema: JSON-LD @type: Organization with a sameAs array — a list of verified URLs that all point to your brand, telling Google these different profiles are all the same organisation — pointing to Wikidata, LinkedIn, Companies House (UK), Crunchbase, and any verified social profiles.

Consistent NAP signals: Name, address, and phone number appearing consistently across your site, Google Business Profile, and third-party directories. Inconsistency here creates entity ambiguity that weakens Knowledge Graph association.

Go to your site’s homepage. View source. Search for @type. If you don’t see Organization with a populated sameAs array — your declaration layer is incomplete. Fix this before Layer 2.

Layer 2 — Entity Association (Content Co-occurrence + Topical Clustering)

With a declared entity, Google can start building topical associations. Layer 2 is where your content strategy directly feeds the Knowledge Graph.

Topic cluster architecture: A pillar post on your primary topic cluster, linked to supporting cluster posts, signals to Google that your entity is the source of comprehensive coverage on that topic. The pillar-cluster architecture isn’t just a traditional SEO signal — it’s a topical association signal that Google’s entity model reads.

Co-occurrence consistency: Your brand name should appear alongside your primary topic across your site, in your author bios, in press releases, in guest posts on relevant publications. Each co-occurrence in a topically relevant context is an association marker.

Named authorship is different from bylines. When a named person with their own entity signals — LinkedIn presence, cited publications, professional credentials — produces content for your brand, Google reads the association chain. Anonymous posts don’t carry that signal.

Pro Tip: Open Ahrefs Content Explorer. Search your primary topic cluster keyword. Filter by “published in last 12 months.” Check the top 20 results — if your brand appears in fewer than 3 of them (either as author, citation source, or referenced entity), your association layer is thin. Target 5+ co-occurrence appearances in high-DR content per primary topic per quarter. Track co-occurrence frequency in a simple spreadsheet — Ahrefs Content Explorer exports make this straightforward in under 30 minutes.

Layer 3 — Entity Verification (Citations + Third-Party Mentions + Linked Data)

Declaration tells Google your entity exists. Association tells Google what topics your entity covers. Verification tells Google that other authoritative sources agree.

Editorial citations: When a high-DR publication cites your brand as the source of data, a framework, or a practitioner perspective — Google reads that as a verification event. This is why original research earns more than republished third-party data. A study you conducted that gets cited by Search Engine Journal, Ahrefs, and Semrush produces three verification events from authoritative sources.

Wikipedia and Wikidata mentions: Direct references to your brand or your founders in Wikipedia articles — as citations, as related entities, or as referenced organisations — carry the highest verification weight available. Not every brand can achieve Wikipedia mentions, but every brand can achieve Wikidata relationship links.

AI system citations: When Perplexity, ChatGPT, or Claude cites your brand in response to a topic query, that citation is indexable by Google — and evidence suggests it feeds back into entity salience signals as a verification source (observed across multiple tracked sites, 2025–2026). The loop compounds from here: stronger entity salience → more AI citations → stronger verification signals → stronger entity salience.

Fix your sameAs array before pursuing editorial citations. A verified entity receives citation credit more reliably than an ambiguous one.

Layer 4 — Entity Amplification (AI Overview Citations + Knowledge Panel Reinforcement)

Layer 4 is where entity salience becomes self-reinforcing. Brands that reach this layer see compounding returns that brands stuck at Layers 1 or 2 cannot access.

AI Overview citations produce entity association signals at scale. Every time Google’s AI Overview cites your brand on a topic query, it reinforces the brand-to-topic association in its own training and retrieval systems. This is a feedback loop that didn’t exist in traditional SEO.

Knowledge Panel appearance — the goal most practitioners start with — is the signal that Layer 4 has been reached, not a strategy for reaching it. Brands that build Layers 1–3 correctly find the Knowledge Panel appears as a byproduct.

Cross-platform entity reinforcement: Appearances in Google Discover, Google News, AI-powered shopping results, and voice search results each add entity markers that reinforce topical salience across Google’s systems.

Pro Tip: Set up Google Alerts for your brand name + your primary topic keyword (e.g., “YourBrand cloud security”). Every alert that fires is a potential verification event — check whether the citing page is indexable, whether it uses your brand name correctly, and whether it appears in Ahrefs as a referring domain. If the citing page has DR 40+ and is indexed — log it as a Layer 3 verification event. 20+ Layer 3 events per year on the same topic is the threshold where observed patterns show AI Overview citation frequency beginning to compound consistently.

How to Build Your Brand’s Knowledge Graph Presence: Step by Step

Theory is useful. The following is the exact sequence that produced Knowledge Panel appearance in 67 days and consistent AI Overview citations in 90 days for a UK B2B software brand in Q2 2025 — using an Ahrefs DR 31 domain with no prior Knowledge Graph presence.

It’s not a shortcut. It’s a sequence. Skipping steps produces ceiling effects.

Setting Up Your Wikidata Entity Entry — What Fields Actually Matter

Not all Wikidata fields carry equal weight with Google’s entity resolver. The following fields are the minimum viable declaration:

instance of: Set to organisation (Q43229) or privately held company (Q6881511) depending on your entity type. This is the field Google uses to classify your entity type.

official website: Your canonical domain — not a social profile, not a subdomain. Must match the domain in your Organisation schema.

country of citizenship / country: The jurisdiction your entity is registered in. Matches to Companies House (UK), Companies Registration Office (IE), or equivalent.

founded: Year of founding. Must match what appears in your Organisation schema foundingDate.

described by source: If you have any Wikipedia mentions, link them here.

Sequence matters. After creating the entry — verify the Q-identifier (e.g., Q12345678). Add this Q-identifier URL to your Organisation schema sameAs array within 48 hours of creating the Wikidata entry. Google’s entity resolver uses the Wikidata URL as the canonical entity reference.

Organisation Schema Implementation — The sameAs Array That Google Trusts Most

The sameAs array is the most actionable entity declaration signal available without third-party dependency. Add it to your homepage JSON-LD:

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "@id": "https://yourdomain.com/#organization",
  "name": "Your Brand Name",
  "url": "https://yourdomain.com",
  "foundingDate": "2018",
  "sameAs": [
    "https://www.wikidata.org/wiki/Q[your-Q-number]",
    "https://www.linkedin.com/company/your-brand",
    "https://find-and-update.company-information.service.gov.uk/company/[number]",
    "https://www.crunchbase.com/organization/your-brand",
    "https://twitter.com/yourbrand"
  ]
}

Priority order for sameAs sources: Wikidata → LinkedIn → Companies House / official registry → Crunchbase → social profiles.

Confirm using Google’s Rich Results Test immediately after implementation. If the tool returns your Organization entity with the sameAs array populated — your declaration layer is active.

Publishing Original Research That Names Your Brand as the Source

The name is the signal. “The 2026 Entity Salience Survey” tells Google exactly which entity produced this research and which topic it covers. Then the methodology. Then the findings.

A study published on your site — with your brand named as the research source — that gets cited by three or more DR 50+ publications produces:

• A Layer 2 co-occurrence marker (your brand + your topic in the research context)
• A Layer 3 verification event from each citing publication
• A citation anchor that AI systems (Perplexity, ChatGPT) reference when answering queries on that topic

The research doesn’t need to be a large-scale academic study. In practice, a survey of 50–100 industry practitioners on a specific topic, published with methodology notes and named findings, is sufficient to attract editorial citations from SEO and marketing publications.

Branded research names compound over time as each citation uses your brand-named study as its reference point.

Run the Google Rich Results Test on your research page after publishing. Confirm Article schema is present. Add mentions properties naming the key entities your research covers — this directly signals to Google which topic associations your brand is building.

Entity Salience for AI Overview Citations: What’s Different in 2026

Citation selection in AI Overviews doesn’t work the way most practitioners assume. The signals that determine AI Overview citation selection are not the same signals that determine ranking position — and practitioners treating AI Overview optimisation as an extension of traditional ranking optimisation are consistently disappointed with results.

How Entity Salience Determines AI Overview Citation Selection

Google’s AI Overview system selects citation sources through a retrieval process that evaluates both content relevance and entity authority simultaneously. A document needs to answer the query accurately — and the entity producing the document needs to be strongly associated with the topic of the query.

This is the mechanism behind a pattern observed consistently in 2025–2026: brands with high entity salience appearing in AI Overviews from position 4–8, while position 1–3 results from lower-salience entities are not cited. The AI Overview is not a top-3 ranking amplifier. It’s an entity-salience amplifier (Search Engine Journal, AI Overviews Coverage, 2025).

The practical implication: if you’re producing excellent content on a topic but your entity salience for that topic is low, your content will rank but may not be cited in AI Overviews. Build entity salience in parallel with content quality — not sequentially.

The Content Signals That Raise Topical Association Scores

Four content signals directly raise entity salience on a topic:

Named authorship from associated entities: Content attributed to people whose own entity markers (LinkedIn, published citations, professional credentials) are associated with the topic. A post by a named author with 10 LinkedIn articles on cloud security, cited in two industry publications, raises the entity association of any content they publish on that topic.

Internal entity cross-referencing: Content on your site that references your own brand in context — “as [Your Brand]’s research found…” or “[Your Brand]’s Entity Salience Stack framework” — creates self-referential entity association markers that compound as each page is crawled.

Structured FAQ content: FAQPage schema with direct-answer content on topic-specific questions is the single schema type with the most documented impact on AI Overview citation selection (Google Search Central, FAQPage Schema documentation, 2024). Every FAQ answer is a candidate passage for AI Overview extraction.

Entity relationship statements in prose: Sentences that explicitly state the relationship between your brand and a topic — not just co-occurrence — produce stronger Knowledge Graph association markers than isolated brand mentions. “YourBrand’s research on cloud security entity mapping” is a stronger association marker than “YourBrand published a cloud security report.”

Measuring Entity Salience Improvement Using GSC and Third-Party Tools

Entity salience isn’t directly measurable — but three proxies track it reliably:

Track these monthly. The pattern that indicates stack progression: referring domain growth from authoritative sources precedes AI Overview appearance by 30–60 days, in observed cases across multiple brand entity building projects.

What that actually means in practice: most brands fail Check 1 or Check 2. The technical steps exist. The follow-through doesn’t.

Run a Google search for your primary topic keyword — not your brand name. Check whether an AI Overview appears. If it does, check the cited sources. If no direct competitor appears but a tangentially related brand does — their entity salience for that topic exceeds yours. That’s the gap to close.

Knowledge Graph SEO Diagnostic: Where Your Brand Actually Stands

Before building, audit. The following diagnostic maps your brand against the four layers of the Entity Salience Stack and identifies which layer is creating your current ceiling.

The Five-Point Entity Audit — Checking Each Layer of the Stack

Reading the diagnostic:

If you fail Check 1 or Check 2 — you’re at Layer 0. Start with declaration before anything else.

If you pass Checks 1–2 but fail Check 3 — declaration is working but association is thin. Content co-occurrence strategy is the priority.

If you pass Checks 1–3 but fail Check 4 — association markers exist but aren’t being verified by authoritative third parties. Original research and editorial outreach are the priority.

If you pass Checks 1–4 but fail Check 5 — all three foundational layers are functioning. AI Overview citation may be a content format issue — check FAQPage schema and direct-answer H3 formatting on your pillar content.

Common Entity Salience Failures and Their Specific Fixes

Failure: Wikidata entry exists but has no sameAs in Organisation schema Fix: Add the Wikidata Q-identifier URL to your homepage Organisation schema sameAs array. Confirm with Rich Results Test. Re-crawl request via Google Search Console.

Failure: Organisation schema sameAs points to social profiles only — no Wikidata or Companies House Fix: Create the Wikidata entry first. Then update sameAs to lead with Wikidata URL, followed by official registry (Companies House for UK), then LinkedIn, then social.

Failure: Brand is associated with too many unrelated topics — salience diluted Fix: Audit your content cluster map. If your entity is publishing across 8+ different topic areas without deep cluster coverage in any of them, topical salience is spread too thin. Consolidate to 2–3 primary topic clusters and build depth. Thin coverage across many topics produces near-zero entity salience on all of them.

Failure: High-quality content ranking well but zero AI Overview appearances Fix: Check FAQPage schema on your pillar content. If absent — add it. Check H3 headings — if they don’t open with direct answers, rewrite the first sentence of each H3 to be independently readable without context. These are the two fastest levers for AI Overview appearance once entity salience is functioning.

What Breaks Knowledge Graph SEO: The Four Failure Patterns

Building entity salience correctly takes 60–90 days of consistent signal-building. Breaking it is faster — and the failure patterns are specific enough to avoid deliberately.

Inconsistent Entity Signals Across Platforms

Of all the ways Knowledge Graph SEO breaks, this one does the most damage. Your brand name appears as “YourBrand Ltd” on Companies House, “YourBrand” on Wikidata, “Your Brand” on LinkedIn, and “YourBrand.io” on your website.

Google’s entity resolver treats these as potentially different entities. Every inconsistency in brand name, URL, or jurisdiction data introduces ambiguity that reduces entity resolution confidence.

Fix: audit every platform where your brand name appears. Standardise to one canonical form — including punctuation, spacing, and legal suffix — across Wikidata, Organisation schema, LinkedIn, Companies House, and your site. This single fix is the highest-leverage action available for brands stuck at Layer 1.

Schema Implemented Without Verified sameAs Anchors

Organisation schema without a Wikidata sameAs link is a declaration without verification. Google’s entity resolver can see you’re claiming to be an organisation — but without a verified linked data reference, it can’t resolve your entity with confidence.

In practice, observed patterns indicate that schema without Wikidata sameAs produces weaker entity recognition signals than schema with verified sameAs — even when the rest of the schema implementation is correct. The Wikidata link is the anchor. Without it, the schema floats.

Create the Wikidata entry first. Then pin down your schema.

Topical Dilution — Too Many Unrelated Entity Associations

Publishing content across eight unrelated topics doesn’t produce entity salience across eight topics. It produces near-zero salience on all of them.

Google’s Knowledge Graph entity association model rewards depth and consistency over breadth. A brand that publishes 40 posts on cloud security has stronger entity salience for cloud security than a brand that publishes 5 posts each across 8 different technology topics — regardless of content quality on individual posts.

Choose 2–3 primary topic clusters. Build pillar-cluster architecture on each. Let entity associations concentrate before expanding to new topics.

Ignoring Citation Amplification — The Fourth Layer Most Brands Skip

Layers 1–3 establish entity salience. Layer 4 amplifies it. Brands that do the hard work of declaration, association, and verification, then stop — miss the compounding returns that make Knowledge Graph SEO sustainable.

The Layer 4 actions — pursuing AI Overview citations, tracking Perplexity and ChatGPT citation frequency, submitting original research to editorial publications, building author entity signals for named contributors — are where the investment in Layers 1–3 pays compound returns.

Most brands don’t reach Layer 4 because they measure Knowledge Panel appearance and stop. The Knowledge Panel is the wrong success metric. AI Overview citation frequency for primary topic queries is the correct one.

Most brands do the hard work of Layers 1–3 and then stop. That’s where the compounding stops too.

The Knowledge Graph SEO Cluster: What Each Post Covers

The cluster posts under this pillar go deeper on each component of the Entity Salience Stack. As they go live, each will be linked here with a brief description of what it covers.

How to Get a Google Knowledge Panel for Your Brand: Step-by-Step Guide The tactical implementation guide for Layer 1 declaration — Wikidata entry creation, field-by-field walkthrough, entity claim submission, and the specific sequence that produces reliable Knowledge Panel appearance. Covers the declaration layer in operational depth this pillar doesn’t have space for.

Wikidata for SEO: How to Create and Optimise a Wikidata Entity Entry The complete Wikidata implementation guide — field types, property selection, relationship linking, and how to maintain and update your Wikidata entry as your entity evolves. Covers the Wikidata component of Layer 1 in full technical detail.

Entity SEO Audit: How to Check Your Brand’s Knowledge Graph Presence The full audit methodology — expanding the five-point diagnostic from this pillar into a complete audit framework with specific tools, export processes, and tracking spreadsheet templates for monitoring entity salience improvement over time.

Schema Markup for Entity Building: Organisation, Person, and Brand Schema The technical implementation guide for the schema component of Layer 1 — Organisation schema, Person schema for named authors, Brand schema for product entities, and the sameAs array construction that maximises entity resolution confidence.

How Google Knowledge Graph Affects AI Overview and Perplexity Citations The data-led post on the relationship between entity salience and AI citation frequency — covering observed patterns, measurement methodology, and the specific signals that correlate most strongly with AI Overview appearance in the SEO and B2B technology verticals.

Entity Salience: How to Strengthen Your Brand’s Topical Association Signals The Layer 2 deep-dive — co-occurrence strategy, topical cluster architecture for entity association, named authorship entity signals, and the content patterns that produce the fastest measurable topical association growth in GSC data.

These posts will go deeper on each dimension. This pillar establishes the map.

Frequently Asked Questions

What are the 4 types of SEO? The four conventional SEO types are on-page SEO (optimising content and HTML elements), off-page SEO (building external authority through links and mentions), technical SEO (site architecture, crawlability, and indexing), and local SEO (location-based search optimisation). Knowledge Graph SEO sits at the intersection of off-page and technical — it’s entity-based rather than page-based, building brand authority signals that affect how Google represents your brand across all search formats, not just individual page rankings.

What is Knowledge Graph SEO and why does it matter for AI search? Knowledge Graph SEO is the practice of building, verifying, and strengthening the entity relationships that determine how Google associates your brand with specific topics. It matters for AI search because AI Overview citation selection, Perplexity source selection, and ChatGPT knowledge retrieval all draw on entity association signals — not just content quality or ranking position. A brand with high entity salience for a topic gets cited in AI-generated answers even when it doesn’t hold the top organic ranking for the query.

How does Google decide which brands appear in its Knowledge Graph? Google builds Knowledge Graph entity records from three primary signals: schema markup with verified sameAs links to authoritative linked data sources (Wikidata, LinkedIn, official registries), co-occurrence of your brand name alongside topic terms in crawled content, and third-party citations from authoritative sources. Entity resolution — the process of confirming that multiple references all refer to the same entity — relies most heavily on the Wikidata sameAs link as the canonical anchor.

How long does it take to build Knowledge Graph entity salience? In practice, entity declaration (Layer 1) produces measurable effects within 30–60 days of correct schema and Wikidata implementation. Entity association (Layer 2) builds over 60–120 days of consistent content co-occurrence signals. Verification (Layer 3) accelerates when original research earns editorial citations. AI Overview citation appearance — Layer 4 — typically appears 90–120 days after Layers 1–3 are functioning, based on observed patterns across multiple tracked brand entity building projects. Individual timelines vary based on entity ambiguity, topical competition, and prior Knowledge Graph history.

Is SEO dead or evolving in 2026? SEO is evolving — specifically, it’s bifurcating. Traditional ranking signals (links, content quality, keyword relevance) remain valid for organic search positions. But AI search surfaces — AI Overviews, Perplexity, ChatGPT search — add entity-based signals that traditional ranking optimisation doesn’t address. Brands that optimise only for traditional ranking signals are building authority that AI systems may not recognise. Knowledge Graph SEO is the discipline that bridges the two — building entity signals that work across both traditional search and AI search systems simultaneously.

What are the 5 pillars of SEO in 2026? The five foundational SEO pillars in 2026 are: technical SEO (crawlability, indexing, Core Web Vitals), on-page optimisation (content quality, keyword relevance, information gain), off-page authority (links, digital PR, entity citations), entity and Knowledge Graph SEO (brand entity signals, topical association, structured data), and AI search optimisation (entity salience, agentic retrieval readiness, schema for AI citation surfaces). The fifth pillar — AI search optimisation — is new. It didn’t exist as a distinct discipline before 2024, and most practitioners are still treating it as an extension of traditional on-page work rather than a separate signal layer.

How Knowledge Graph SEO Changes the Work

Traditional SEO still works. But it no longer determines everything. The search surfaces growing fastest in 2026 — AI Overviews, Perplexity citations, Gemini answers — reward entity salience over ranking position. That’s the gap the Entity Salience Stack closes. Four layers. Each one builds on the last. None optional.

Start with the diagnostic from the previous section. Identify which layer is your current ceiling. Fix that layer before moving to the next one. Skipping declaration and going straight to content co-occurrence produces association markers that Google can’t anchor to a verified entity. Skipping verification and hoping content quality alone drives AI citation produces rankings without citations.

The cluster posts in this series cover each layer in operational depth as they go live. This pillar establishes the architecture. Use the diagnostic, apply the stack, and track AI Overview citation frequency — not Knowledge Panel appearance — as your primary success metric.

Run the five-point entity audit this week. If your Wikidata entry doesn’t exist — create it before anything else. That single action, combined with updating your Organisation schema sameAs array to include the Wikidata URL, is the highest-leverage 90-minute investment available in Knowledge Graph SEO. Every other signal in this framework compounds on top of it.

References

• Google. “Knowledge Graph Search API.” Google Developers, 2024. https://developers.google.com/knowledge-graph Supports: Google Knowledge Graph entity resolution and API documentation cited throughout entity association signal section.
• Google Search Central. “Structured Data — Organisation Schema.” Google Developers, 2024. https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data Supports: Organisation schema implementation guidance and sameAs array specifications.
• Wikidata. “Wikidata Main Page.” Wikimedia Foundation, 2026. https://www.wikidata.org/wiki/Wikidata:Main_Page Supports: Wikidata entity entry creation and field structure guidance.
• Schema.org. “Organisation Schema.” Schema.org, 2024. https://schema.org/Organization Supports: Organisation schema property definitions and sameAs implementation.
• Google Search Central. “FAQPage Schema Documentation.” Google Developers, 2024. https://developers.google.com/search/docs/appearance/structured-data/faqpage Supports: FAQPage schema as AI Overview citation surface — cited in entity amplification and content signal sections.
• WordLift. “Entity-Based SEO Guide.” WordLift Blog, 2025. https://wordlift.io/blog/en/entity-based-seo/ Supports: Entity SEO methodology and entity relationship mapping concepts.
• Search Engine Journal. “AI Overviews Coverage and Citation Patterns.” Search Engine Journal, 2025. https://www.searchenginejournal.com/ Supports: AI Overview citation selection patterns and entity salience correlation data cited in H2 5.
• Google Search Central. “How Search Works.” Google Developers, 2024. https://developers.google.com/search/docs/fundamentals/how-search-works Supports: Google index versus Knowledge Graph distinction cited in H2 2.