AEO & Entities: How to Map Your Site’s Entity Graph for 2026 Answer Engines

Stop losing traffic to AI answers: map your entities or get invisible

If your organic traffic has plateaued in 2025–26, the cause may no longer be purely on-page keywords or links. Search behaviour has shifted: audiences ask AI-driven answer engines for summarised answers, and those engines rely on structured entity graphs and provenance signals to decide which sources to cite. The result: pages that aren’t explicitly modelled as entities never get used in AI answers — even when they rank well in traditional SERPs.

This article gives a step-by-step, technical method for mapping the entities on your site, connecting them to content and structured data (JSON‑LD), and measuring which entities are actually driving AI answers in 2026. It’s written for in-house SEOs, technical consultants and agencies who need an actionable entity audit that delivers measurable ROI.

Why entity mapping matters right now (2026 trends)

Late 2025 and early 2026 cemented two trends that change SEO priorities:

• Answer engines prioritise provenance: AI answers now prefer sources that can be tied to stable entity identities and clear provenance trails (citations, credentials, sameAs links).
• Entity-first indexing: Engines increasingly consume structured graphs (Wikidata, Knowledge Graphs, site JSON‑LD) to assemble answers. Entities — not isolated pages — are the primary unit of retrieval for many AI features.

Because of this, your site needs an internal entity graph: a canonical set of things (people, products, services, topics, locations) mapped to pages, schema, and signals that prove authority.

Overview: The entity-mapping workflow (quick)

1. Inventory: extract candidate entities from site content and logs.
2. Normalize: map names to canonical identifiers (Wikidata QIDs, internal IDs).
3. Graph: build a node/edge model linking entities to pages, authors and external IDs.
4. Schema alignment: add/enrich JSON‑LD and sameAs links for each entity node.
5. Signals audit: checklist of citations, co‑occurrence, media, internal links.
6. Measurement: instrument entity-level metrics and monitor AI answer share.
7. Iterate: use findings to prioritise content updates and PR for high-value entities.

Step 1 — Inventory: find the entities already on your site

Start by extracting named entities from your content corpus and logs. You want a superset of everything the site mentions as a distinct thing: brand names, product SKUs, people, locations, processes and high‑value concepts (e.g., “electrostatic spray disinfection”).

How to extract

• Run an automated crawl (Screaming Frog, Sitebulb or a headless crawler) and export all textual content.
• Use an NLP pipeline (spaCy, Hugging Face NER models or Google Cloud Natural Language) to extract PER, ORG, GPE, PRODUCT and custom entity types.
• Cross-reference server logs and Search Console queries to find search phrases that suggest entities users look for.
• Include product feeds, knowledge panel data, and PR assets to capture non-page entities (events, reports).

Output: a CSV with columns [entity_name, entity_type, source_page, mention_count, first_seen, last_seen].

Step 2 — Normalize: consolidate duplicates and assign canonical IDs

Entity names have variants. Normalisation creates a unique identity for each entity. This is essential when feeding a knowledge graph or schema.

Normalization checklist

• Standardise names (UK spellings, punctuation, abbreviations).
• Map to external identifiers where possible: Wikidata QIDs, ISINs, GTINs or internal product IDs. Prefer Wikidata for public entities because many answer engines consult it.
• Resolve aliases (e.g., “Acme Ltd.” = “Acme”) and mark redirects.
• Assign entity confidence scores (high, medium, low) based on external mapping success and mention frequency.

Tools: OpenRefine for clustering, custom Python scripts for QID lookups (Wikidata SPARQL), or toolkits built into SEO platforms that now expose entity matching — or use a one-page stack audit to remove noisy tooling and keep only the matchers that matter.

Step 3 — Build the site entity graph

Turn the normalized list into a graph. Nodes represent entities and pages; edges represent relationships (author_of, mentions, parent_product, competitor_of).

Minimum viable graph model

• Node types: Entity (person, product, service, place, concept), Page, ExternalResource (Wikidata, Wikipedia, PDF).
• Edge types: mentioned_on, canonical_page, authored_by, sameAs, cites, competitor_of.

You can store the graph in a simple spreadsheet, Neo4j, or a lightweight graph DB. Even a Google Sheet with adjacency lists works for small sites.

Practical tip: add a column on each page row with the canonical entity IDs the page represents. That makes mapping to JSON‑LD and analytics trivial.

Step 4 — Align schema: inject canonical entity JSON‑LD

Once you have canonical IDs, enrich the pages that represent each entity with JSON‑LD. The goal is to expose the same entity identity in machine-readable form.

What to include in JSON‑LD

• Type (schema.org type matching entity_type: Product, Person, Organization, LocalBusiness, CreativeWork).
• name, description, url, image, sku/identifier where applicable.
• sameAs array — include authoritative external IDs (Wikipedia, Wikidata QID URL, official profiles, company registration where relevant).
• mainEntityOfPage linking to the canonical page.
• author and publisher structured objects for authored content (with sameAs links for author profiles).

Example: for a product page, embed a Product JSON‑LD with sku, brand (Organization) and sameAs pointing to the product’s Wikidata and manufacturer page.

Note: as of 2026, answer engines pay attention to stable external identifiers. Adding QIDs and sameAs links increases the chances your entity is recognised and cited.

Step 5 — Signals audit: prove the entity's authority

Schema alone is not enough. Answer engines evaluate signals that show authority and provenance. Run an audit on each high-priority entity and score them against a checklist.

Entity signals checklist

• Internal: canonical page exists, clear internal link hierarchy, structured metadata, author attribution, up-to-date content.
• External: authoritative citations (press, journals, government), backlinks to canonical page, sameAs links on authoritative profiles.
• Multimodal: high-quality images with captions, transcripts for audio/video, OCR-friendly text overlays for images used as evidence.
• Provenance: date stamps, version history, credentials for claims (citations, studies, references). Consider secure storage and access governance for your evidence bundles as described in the Zero‑Trust Storage Playbook.
• Social & PR: consistent mentions on social platforms, citations in industry sites, signals of public interest (mentions & engagement).

Score entities (0–100). Use scores to prioritise remediation and PR outreach.

Step 6 — Implement structural and content fixes

Fix the high-impact gaps found in the signals audit. Focus on quick wins first.

Priority fixes

1. Create or consolidate canonical pages for high-value entities (avoid thin fragments).
2. Ensure JSON‑LD uses canonical QIDs and sameAs links.
3. Improve internal linking so entity pages are discoverable from relevant hubs and category pages.
4. Add provenance: link to sources, attach PDFs or datasets, include author bios with credentials.
5. Publish supporting assets (press releases, whitepapers) that cite the canonical entity URL.

In many cases, adding a single authoritative citation or a high‑quality image with captions can move an entity from not‑recognised to recognised by answer engines.

Step 7 — Measure: which entities drive AI answers?

Standard SEO metrics don’t capture entity-level usage in AI answers. You need an entity-centric measurement framework.

Key metrics (entity-level)

• Entity Answer Share (EAS): percentage of monitored AI answers that cite your entity as a source.
• AI Impression Share: number of AI answer impressions tied to queries mapped to the entity divided by total AI answer impressions for that topic.
• Provenance Cite Rate: proportion of AI answers that include a direct citation/backlink to your canonical URL.
• Entity Click Rate: clicks from answer features back to your canonical page (where available).
• Conversion per Entity: goal completions attributable to traffic that entered via AI answers for queries mapped to that entity.

How to measure practically

1. Construct a watchlist of high-priority queries and prompts that map to your entities (seed from Search Console queries and customer intents).
2. Run automated probes against answer engines (Google Generative, Microsoft Copilot, other vertical AIs). Capture the answer text, cited sources and any visible links. Use rotating IPs and follow terms of service.
3. Parse captured answers with an NER model to extract entity mentions and cited URLs, and match cited URLs to canonical entity IDs.
4. Store daily snapshots and compute EAS and AI Impression Share trends.
5. Combine with GA4/analytics: tag pages with entity IDs in dataLayer and create custom dimensions to attribute sessions to entity pages.

Practical tooling: open-source scrapers + LLMs for answer parsing, a lightweight graph DB for mapping, and dashboarding via Looker Studio or internal BI. If your platform is noisy, run a quick stack audit to remove underused tools and cut costs before you instrument entity telemetry.

Measure at the entity level, not just the page. AI engines assemble answers from entities — if you can’t measure entity usage, you can’t optimise for AEO.

Step 8 — Attribution and experiments

After instrumentation, run A/B style experiments at the entity level:

• Change provenance signals (add a citation or sameAs link) for a subset of entities and monitor EAS and Provenance Cite Rate.
• Improve schema richness on some entity pages (detailed properties, images) and compare Entity Click Rate.
• Run a PR campaign that builds external citations for targeted entities and measure AI Impression Share uplift — pair technical fixes with story-led launches or targeted outreach that mentions the canonical entity name exactly.

Use short test windows (4–8 weeks) and control groups. AI answer engines evolve quickly — iterative testing wins. If you need a rapid test model, use a micro-event launch sprint style cadence to release changes and measure impact quickly.

Prioritisation: which entities to optimise first

Don’t try to fix everything. Prioritise by commercial impact and technical opportunity.

• High commercial intent + low provenance score = immediate priority.
• Entities driving organic conversions but with low AI presence = medium priority.
• Low traffic, high-cost entities only if long-term strategic value exists.

Example scoring model: Priority = (CommercialValue * SearchVolume) / (EntityAuthorityScore + 1).

Advanced tactics for 2026

1. Use external identifier networks

Link entity pages to persistent identifiers (Wikidata, VIAF, GTIN). AI answer engines increasingly treat these as trust anchors, especially for factual answers.

2. Build provenance bundles

When publishing claims, bundle supporting evidence: PDF whitepapers, datasets with DOIs, timestamped versions and clear author credentials. Bundles give AI answers verifiable trails; secure these bundles and control access using practices from the Zero‑Trust Storage Playbook.

3. Multimodal entity assets

AI answers are multimodal. Provide tagged images, alt text that references the entity ID, video transcripts and structured captions. That raises the chance your assets are used in visual and mixed-media answers.

4. Digital PR for entity authority

Target placements that include canonical links to your entity pages and authoritative mentions that include the entity name exactly as in your schema. Consistent naming matters — pair these efforts with transmedia and syndicated feeds thinking when the entity sits inside a larger content IP.

Common pitfalls and how to avoid them

• Don’t treat schema as a checkbox. JSON‑LD without supporting citations and internal structure rarely moves the needle.
• Avoid creating many thin canonical entity pages. Consolidate and expand — depth matters for AI engines that evaluate evidence quality.
• Don’t rely solely on 3rd-party platforms. Capture and store answer snapshots yourself for defensible measurement — marketplaces and platforms evolve; see playbooks for marketplace onboarding and measurement like cutting seller onboarding time.
• Be careful with automated scraping of answer engines — ensure compliance with terms of service and regional regulations (UK/EU data rules).

Real-world example (short case study)

Client: UK B2B software provider. Problem: excellent rankings for product pages but no presence in AI answers for “best X software for Y”.

1. Inventory found 120 product entities with inconsistent naming and missing sameAs links.
2. Normalized to Wikidata entries for competing standardised software names and added QIDs to JSON‑LD.
3. Built canonical product pages with detailed specs, PDFs (DOI-like), and clear author credentials.
4. Measured: within 8 weeks, Entity Answer Share rose from 0% to 18% on monitored queries; AI-driven leads increased by 23% quarter-over-quarter.

Outcome: the combination of canonical IDs, provenance bundles and targeted PR moved the client from invisible to regularly cited in AI answers.

Checklist: 10 things to ship in your first 90 days

1. Entity inventory CSV exported from site and logs.
2. Normalized entity list with QIDs and internal IDs.
3. Graph model (spreadsheet or DB) linking entities to pages and assets.
4. JSON‑LD templates updated to include sameAs and canonical entity IDs.
5. Internal linking updates to surface canonical entity pages.
6. Provenance bundles (1–3 PDFs or datasets) attached to top entity pages.
7. Entity-level instrumentation in GA4 (custom dimension) and dataLayer tags.
8. Automated answer scraping for a 50-query watchlist and parsing pipeline.
9. Priority roadmap: top 10 commercial entities with remediation tasks.
10. Baseline report: EAS, AI Impression Share, Provenance Cite Rate for monitoring.

Final thoughts — the future-proof approach

Answer Engine Optimisation is not a one-time update. It’s a structural shift: think in terms of entities, provenance and graphs instead of isolated pages and keywords. Organisations that model their knowledge, publish verifiable evidence and measure entity usage will be the ones that consistently show up in AI answers in 2026 and beyond.

If you take one thing away: build your canonical entity map, expose stable identifiers in JSON‑LD, and measure entity-level usage in AI answers — it converts visibility into measurable value.

Call to action

Need help mapping your site’s entity graph or proving which entities drive AI answers? We run focused 8–12 week entity audits that deliver a prioritised roadmap, JSON‑LD rollouts and an AI answer monitoring setup with dashboards for stakeholders. Contact us for a scoping call and a sample entity audit report tailored to your site.

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