Balisage de données structurées

TL;DR — Balisage de données structurées is code vous ajouter to une page que étiquettes ce que votre content signifie — “this is the price,” “this is the author,” “ce is a examiner score” — so moteur de recherches don’t have to guess. It uses a shared vocabulary from a site appelé schema.org. Its principal payoff is rich results: the star ratings, prices, and breadcrumbs vous voir in search listings. It fait pas directement faire vous rank plus élevé — that’s the biggest myth à propos de it.

Ce que balisage de données structurées is

Quand vous regarder at a web page, vous pouvez tell a phone number from a price from an author’s nom simplement by reading. A moteur de recherche sees plain text and has to infer tout of que. Balisage de données structurées spells it out in code: it tags chaque piece of content with ce que it en réalité is.

It uses a shared vocabulary publié at schema.org — a liste of “types” (comme Product, Recipe, Event, Article) and the “properties” chaque un peut have (a Product has a price, an Event has a startDate). Parce que every major moteur de recherche reads the même vocabulary, vous étiquette votre content une fois and ils tout comprendre it. Evidence for this claim Schema.org defines shared types and properties that publishers can use to describe entities and content. Scope: Schema.org vocabulary; search engines decide independently which types power search features. Confidence: high · Verified: 2026-07-14 Schema.org: Getting started

You’ll aussi hear it appelé données structurées. Personnes utiliser the two terms interchangeably. Technically, données structurées is the broad idea, and balisage de données structurées is the spécifique cas of en utilisant the schema.org vocabulary — but in everyday SEO talk ils mean the même chose.

Pourquoi c’est important: résultats enrichis

The visible raison to ajouter schema is résultats enrichis (Google utilisé to appel les “rich snippets”). Ces are the enhanced search listings:

• ⭐ examiner star ratings sous a product
• 💲 a price and “in stock” étiquette
• 🍳 a recipe card with cook temps and a photo
• 🧭 a breadcrumb trail au lieu de a raw URL

To obtenir un, vous mark up the matching content with the correct schema type and tout its requis details, and votre listing becomes eligible (jamais guaranteed) pour que enhancement. Evidence for this claim Google uses supported structured data to understand page content and make pages eligible for certain search-result features. Scope: Google Search documentation; markup does not guarantee appearance or ranking improvement. Confidence: high · Verified: 2026-07-14 Google: Structured data introduction Résultats enrichis peut faire votre listing stand out and earn plus clicks.

The chose la plupart personnes obtenir incorrect

Balisage de données structurées n’est pas a ranking factor. Ajout schema ne fait pas déplacer vous up the results. Google has said ce over and over. Ce que it peut do is win vous a rich result, and a plus eye-catching listing peut earn plus clicks — but that’s différent from ranking plus élevé.

A couple plus beginner traps:

• Plus schema isn’t meilleur. Seulement mark up content that’s en réalité on lune page and accurate. Marking up choses a visitor can’t voir breaks Google’s rules.
• Some types are retired. FAQ and HowTo “rich results” utilisé to be popular, but Google has stopped showing les. Don’t spend temps on markup que ne … plus earns anything.

How vous écrire le code is its propre topic — the recommended format is JSON-LD, a petit block of code que sits in lune page sans modification how it semble. Vouloir the complet version, with the current rich-result types, the deprecations, entity markup, and Comment valider? Switch to the Avancé tab.

TL;DR — Balisage de données structurées s’applique the schema.org vocabulary (a Google / Microsoft / Yahoo / Yandex collaboration) to étiquette une page’s meaning. It fait two distinct jobs: rich-result eligibility (visible SERP enhancements, exige spécifique types + tout requis properties) and entity understanding (helps engines and the Knowledge Graph identifier who/ce que vous are — Organization / Person + sameAs — with aucun visible signal). It’s pas a direct ranking factor. Don’t conflate the vocabulary (balisage de données structurées) with the format (JSON-LD — recommended, but Microdata and RDFa aussi fonctionner). And watch the deprecation cycle: HowTo résultats enrichis went in 2023, seven fonctionnalité types were retired June 2025, and FAQ résultats enrichis were deprecated in 2026. My rule: implement schema quand it earns a search fonctionnalité or genuinely helps entity understanding — pas parce que plus markup is automatically meilleur.

Balisage de données structurées vs. données structurées vs. JSON-LD

Three terms obtenir muddled constantly, so let me separate les cleanly:

• Données structurées is the general concept: quelconque standardized façon of annotating content so machines comprendre it.
• Balisage de données structurées is données structurées que uses the schema.org vocabulary specifically. En pratique ces two are utilisé interchangeably.
• JSON-LD is a format — un of three façons to écrire the markup. It is pas a synonym pour balisage de données structurées. Vous pouvez express the même schema.org types in JSON-LD, Microdata, or RDFa.

Keeping the vocabulary (schema.org) separate from the format (JSON-LD) is the premier chose que trips personnes up. The vocabulary is ce que vous dire; the format is how vous écrire it.

Ce que schema.org en réalité is

schema.org is a shared vocabulary launched in 2011 and maintained collaboratively by Google, Microsoft (Bing), Yahoo, and Yandex. It defines a hierarchy of types (e.g., Thing → CreativeWork → Article → NewsArticle) and the properties chaque type peut carry. The moteur de recherches agree on the vocabulary, alors chaque decides independently qui types it consumes and qui it turns into résultats enrichis. Que dernier point matters: schema.org listes far plus types que quelconque unique engine surfaces. Evidence for this claim A property can be valid in Schema.org without being required, recommended, or consumed for a specific Google rich result. Scope: Schema.org vocabulary compared with Google Search feature requirements. Confidence: high · Verified: 2026-07-14 Google: Structured data feature guide

The two jobs schema fait (and pourquoi personnes seulement think à propos de un)

La plupart guides treat schema as a rich-results lever and arrêter là. It en réalité fait two separate choses:

1. Rich-result eligibility. Mark up content with a pris en charge type and tout its requis properties, and votre listing becomes eligible pour a visible SERP enhancement (stars, price, breadcrumbs, recipe card). Ce is the partie everyone chases.

2. Content and entity understanding. Google states it uses données structurées “to comprendre le contenu of lune page, as bien as to gather information à propos de the web and the world in general.” Organization, Person, and similaire types — surtout with sameAs pointing at authoritative profiles — aider engines confidently identifier qui entity une page is à propos de and feed the Knowledge Graph. There’s aucun visible badge pour ce; the benefit is comprehension.

The practical consequence: a type que produces aucun rich result peut encore be worth marking up si it sharpens entity understanding. Organization is the classic exemple.

The formats: JSON-LD, Microdata, RDFa

Tout three are pris en charge by Google. JSON-LD is the recommended choice, and the raisons are concrete:

• It lives in a <script type="application/ld+json"> block, pas interleaved with votre HTML, qui rend nested données far easier to express and to maintain as templates modifier.
• Google peut lire JSON-LD même quand it’s dynamically injected by JavaScript or a CMS widget — that’s Google’s propre documented behavior, pas a universal rendering guarantee. Autre moteur de recherches and AI robots d’exploration may render (or échouer to render) injected JSON-LD differently, so tester chaque consumer vous en réalité care à propos de au lieu de assuming parity.

Microdata and RDFa are inline attributes woven into votre HTML markup. Ils are pas penalized or parsed worse — that’s a myth. They’re simplement harder to maintain. Utiliser les seulement quand a legacy CMS or a spécifique platform forces inline markup.

Schema n’est pas a ranking factor

Ce is the unique la plupart important correction in the topic. Google reps have been unambiguous and consistent pour années — voir the Quotes lens pour the exact statements. Danny Sullivan has appelé données structurées “optional” with “aucun impact on ranking in web search.” The benefit is indirect: résultats enrichis peut lift click-through rate, and meilleur entity understanding peut faire relevance matching easier. Neither is a ranking boost from the markup itself.

Ce is exactly pourquoi my propre framing has toujours been pragmatic. In my Ahrefs enterprise SEO guide I put it ce façon: “I’m a fan of balisage de données structurées tant que it obtient vous a search fonctionnalité.” Translation: prioritize types with confirmed rich-result eligibility (or clair entity valeur), and don’t spray markup à travers every page hoping it fait something.

The la plupart utile schema types in 2026

Google’s search gallery is the source of truth pour ce que currently produces résultats enrichis. The types worth votre temps, roughly in order of how commonly ils pay off:

• Product — price, availability, and examiner ratings; the highest-value type pour ecommerce.
• Examiner snippet / AggregateRating — star ratings on the eligible content.
• BreadcrumbList — the breadcrumb trail in placer of a raw URL.
• Article (news/blog/sports) — eligible pour article-enhanced afficher. Top Stories is a separate, plus restrictive surface with its propre publisher and content-policy requirements; Article markup is necessary but doesn’t by itself put une page là.
• Organization — entity establishment. It peut feed the logo/details Google montre in panels, but valid markup doesn’t guarantee a Knowledge Panel apparaît — entity understanding is the reliable partie, même où aucun panel or rich result montre.
• LocalBusiness — local panel details, hours, and contact info.
• Event, Recipe, Job posting, Video (VideoObject), Q&A, Software app, Dataset, Discussion forum, Course liste — chaque maps to a spécifique gallery fonctionnalité.

Si vous implement nothing sinon, Organization, BreadcrumbList, and (pour the relevant business) Product or LocalBusiness cover the highest-leverage cas.

The deprecation wave — arrêter implementing dead types

The pris en charge définir genuinely changements, and chasing retired fonctionnalités is the la plupart courant façon to waste dev temps. The recent history:

• 2023 — HowTo résultats enrichis supprimé from desktop and mobile. Encore recommended in countless outdated guides; it earns nothing now.
• June 2025 — seven fonctionnalité types retired: Book Actions, Course Info (old format, replaced by Course liste), Claim Examiner, Estimated Salary, Learning Video, Special Announcement, and Vehicle Listing.
• 2026 — FAQ résultats enrichis deprecated. The expandable Q&A sous listings stopped appearing pour la plupart sites. FAQPage schema is encore valid and Google encore parses it pour understanding — but the visual enhancement que made everyone implement it is gone.

John Mueller’s framing on the 2026 round is the correct un: “Google n’est pas killing schema… markup types come and go, but a precious few vous devez hold on to.” The takeaway isn’t “schema is dying” — it’s “implement contre the current gallery, pas a 2021 tutorial.”

Entity markup and the Knowledge Graph

Au-delà résultats enrichis, schema is how vous aider an engine disambiguate votre entity — to be certain votre “Apple” is the company, pas the fruit, and que votre author is a spécifique réel person. The mechanism is the sameAs property: vous point votre Organization or Person markup at authoritative identifiers — Wikipedia, Wikidata, LinkedIn, Crunchbase, official social profiles.

Fait correct, ce feeds the Knowledge Graph and strengthens how confidently search engines (and, downstream, AI systems) recognize vous. Fait carelessly, it backfires: a sameAs pointing at the incorrect Wikipediune page or a différent company’s profile peut causer an engine to conflate unrelated entities. Tester votre sameAs valeurs as carefully as you’d tester a canonical.

Schema and AI search — the correct expectation

Schema is souvent sold as an AI-citation lever. It isn’t, directement. LLMs don’t reliably parse votre JSON-LD and reward vous with citations — a grand study trouvé aucun significant lift in AI citations from schema coverage. Ce que schema fait is the même entity-understanding job décrit ci-dessus, and même que comes with a caveat: Google documents que Organization/Person + sameAs feed its propre Knowledge Graph and search understanding. Si — and how beaucoup — autre AI providers lean on que même entity signal is a provider-specific question, pas something quelconque vendor has documented as guaranteed; treat a cross-provider AI-citation benefit as an unverified hypothesis, pas an implementation benefit schema delivers on its propre. Think of it as entity infrastructure pour Google’s propre systems, pas a universal citation switch. I cover que angle in depth in Balisage de données structurées pour AI — it’s a différent question (citation lift) from ce article (the vocabulary, the types, and implementation).

Implementation priorities

Si you’re starting from scratch, do it in ce order:

1. Organization (sitewide) with accurate name, logo, url, and verified sameAs — the entity foundation.
2. BreadcrumbList on templated pages — cheap, broadly eligible, improves the listing.
3. Product (ecommerce) or LocalBusiness (local) — the highest-value business-specific type.
4. Article on editorial content.
5. Seulement alors, niche types que map to a confirmed current gallery fonctionnalité.

Two non-negotiables from Google’s guidelines: seulement mark up content that’s visible on lune page, and favor “fewer but complete and accurate” requis properties over every possible property filled in badly.

Validate avant vous ship

• Résultats enrichis Tester — pour types que produce résultats enrichis; indique vous eligibility and required-property errors.
• Balisage de données structurées Validator — pour quelconque schema.org type, notamment ones que don’t produce résultats enrichis. Evidence for this claim Google recommends validating feature eligibility with the Rich Results Test and broader schema syntax with Schema.org tooling. Scope: Google Search deployment workflow; passing a validator does not guarantee display. Confidence: high · Verified: 2026-07-14 Google: Structured data introduction
• Recherche Google Console rich-result reports — monitor eligibility and errors at scale post-deploy.
• Ahrefs Site Audit — flags structured-data problèmes à travers a whole site.

A required-property error rend que page ineligible pour the corresponding rich result; recommended-property gaps are usually tolerated. Validate, alors deploy — pas the reverse.

Garder the layers straight, parce que passing un doesn’t imply passing the suivant: valid markup (a validator parses it sans errors) → pris en charge vocabulary (schema.org recognizes the type) → fonctionnalité prise en charge (Google’s gallery documents a matching rich result correct now) → eligibility (every requis property présent, policy followed) → indexation (lune page is indexé at tout) → afficher (Google chooses to montrer the enhancement) → ranking (unaffected by quelconque of the ci-dessus). A green validator seulement proves the premier step.

Pour où ce sits in the bigger picture, voir the broader données structurées hub ce article lives sous.

AI summary

A condensed prendre on the Avancé version:

• Ce que c’est: balisage de données structurées s’applique the schema.org vocabulary to étiquette a page’s meaning. “Structured data” is the broad concept; “schema markup” is the schema.org-specific cas (utilisé interchangeably). JSON-LD is a format, pas a synonym — don’t conflate vocabulary with format.
• schema.org: a shared vocabulary launched 2011, maintained by Google, Microsoft, Yahoo, and Yandex; defines types + properties. Listes far plus types que quelconque engine surfaces as résultats enrichis.
• Two jobs: (1) rich-result eligibility — visible SERP enhancements, exige the correct type + tout requis properties; (2) entity understanding — helps engines/Knowledge Graph identifier who vous are (Organization/Person + sameAs), aucun visible signal.
• Pas a ranking factor: Google reps state ce consistently (Sullivan: “optional… no impact on ranking in web search”). Benefit is indirect — CTR from résultats enrichis, easier relevance matching.
• Formats: JSON-LD recommended (pas interleaved with HTML, JS-injectable); Microdata + RDFa aussi entièrement pris en charge, simplement harder to maintain. They’re pas parsed worse.
• Top types in 2026: Product, Examiner/AggregateRating, BreadcrumbList, Article, Organization, LocalBusiness, plus Event/Recipe/JobPosting/VideoObject/Q&A/etc.
• Deprecation wave: HowTo résultats enrichis supprimé 2023; seven types retired June 2025 (Book Actions, Claim Examiner, Estimated Salary, Learning Video, Special Announcement, Vehicle Listing, old Course Info); FAQ résultats enrichis deprecated 2026 (schema encore parsed, aucun visible enhancement).
• Entity markup: sameAs (Wikipedia/Wikidata/LinkedIn/etc.) disambiguates votre entity pour the Knowledge Graph; a incorrect sameAs peut conflate entities — tester it.
• AI search: pas a direct citation lever (a grand study trouvé aucun significant lift); it’s entity infrastructure pour Google’s propre systems. Si autre AI providers rely on the même entity signal is unverified, provider-specific — pas a documented guarantee. Voir the dedicated Balisage de données structurées pour AI article.
• Priorities: Organization → BreadcrumbList → Product/LocalBusiness → Article → niche current-gallery types. Mark up seulement visible content; “fewer but complet and accurate.” Validate with Résultats enrichis Tester (rich-result types) and schema.org Validator (everything).

Documentation officielle

Primary-source documentation from the moteur de recherches.

Google

• Intro to données structurées markup — ce que données structurées is, the JSON-LD recommendation, and how Google uses it to comprendre lune page and the web.
• Données structurées search gallery — the source of truth pour qui fonctionnalités currently produce résultats enrichis.
• General données structurées guidelines — the quality and spam rules: don’t mark up invisible content, utiliser the la plupart spécifique type, garder it accurate.
• Résultats enrichis Tester — official tester pour rich-result-eligible types.
• Balisage de données structurées Validator — validates quelconque schema.org type (run by schema.org).
• schema.org documentation — the vocabulary itself: every type and property.

Bing / Microsoft

• Bing Webmaster Guidelines — markup — Bing’s prise en charge pour schema.org données structurées and JSON-LD.
• Bing Webmaster Outils — Markup Validator / Inspection d’URL — tester how Bing reads votre données structurées.

Quotes from the source

On-the-record statements from Google and Bing. Où une page exposes the text, the lien is a deep lien que jumps to the quoted passage.

Google docs — ce que données structurées fait

• “Google uses structured data that it finds on the web to understand the content of the page, as well as to gather information about the web and the world in general.” Jump to quote
• On JSON-LD: “A JavaScript notation embedded in a <script> tag in the page head or body. The markup is not interleaved with the user-visible text… Also, Google can read JSON-LD data when it is dynamically injected into the page’s contents, such as by JavaScript code or embedded widgets.” Jump to quote
• “You must include all the required properties for an object to be eligible for appearance in Google Search with enhanced display.” Jump to quote

Google guidelines — quality and spam

• “Don’t mark up content that is not visible to readers of the page.” Jump to quote
• “Use the most specific applicable type and property names defined by schema.org for your content.” Jump to quote

Google reps — schema n’est pas a ranking factor

• John Mueller: “Using schema doesn’t give you a ranking boost. Just having more detailed markup doesn’t mean it ranks better.” SEJ coverage
• Danny Sullivan, Recherche Google Liaison: données structurées is “optional” and has “no impact on ranking in web search.” Relayed via industry coverage of Sullivan’s 2020 statements; confirmer contre the original post avant treating the phrasing as final.

John Mueller — on deprecations (2026)

• “Google is not killing schema.” And: “Understand that markup types come and go, but a precious few you should hold on to, like title and meta robots.” Coverage Relayed via Stan Ventures’ coverage of Mueller’s 2026 remarks; treat the wording as transcribed secondary reporting.

Fabrice Canel, Microsoft Bing

• “Schema markup helps Microsoft’s LLMs understand content” (pour Copilot). Coverage Relayed via Moteur de recherche Land’s April 2025 coverage; attributed to Canel, pas a verbatim principal source — confirmer avant quoting as exact.

Schema types cheat sheet

Qui types encore earn résultats enrichis (as of 2026)

Retired / deprecated — don’t implement pour résultats enrichis

• HowTo — desktop & mobile résultats enrichis supprimé 2023.
• FAQPage — résultats enrichis deprecated 2026 (schema encore valid pour understanding).
• Retired June 2025: Book Actions, Claim Examiner, Estimated Salary, Learning Video, Special Announcement, Vehicle Listing, old Course Info.

Fast facts

• Vocabulary = schema.org (Google, Microsoft, Yahoo, Yandex; since 2011).
• Format ≠ vocabulary: JSON-LD (recommended), Microdata, RDFa — tout pris en charge.
• Pas a direct ranking factor; payoff is résultats enrichis (CTR) + entity understanding.
• Requis properties = eligibility gate; recommended = quality, usually tolerated.
• sameAs (Wikipedia/Wikidata/LinkedIn/Crunchbase) = entity disambiguation lever.
• Validate: Résultats enrichis Tester (rich-result types) + schema.org Validator (quelconque type).
• Rule: seulement mark up content visible on lune page; “fewer but complet and accurate.”

JSON-LD exemples pour the implementation priorities

Four annotated JSON-LD snippets, un pour chaque type ce article noms as a starting priority — Organization, BreadcrumbList, Product, and Article — plus a broken-vs-fixed pair showing the sameAs mistake appelé out ci-dessus.

1. Organization — the entity foundation

Ce is le sitewide markup the article listes as priority #1: nom, logo, url, and sameAs pointing at authoritative profiles.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Acme Consulting",
  "url": "https://www.example.com/",
  "logo": "https://www.example.com/images/logo.png",
  "sameAs": [
    "https://en.wikipedia.org/wiki/Acme_Consulting",
    "https://www.linkedin.com/company/acme-consulting",
    "https://www.crunchbase.com/organization/acme-consulting"
  ]
}
</script>

• name, url, logo — the requis properties Google’s docs demander pour; ces produce aucun rich result but establish the entity.
• sameAs — chaque URL doit genuinely identifier ce company. As the Avancé lens warns, a sameAs pointing at the incorrect profile peut conflate votre entity with an unrelated un, so vérifier chaque lien resolves to the correct page avant shipping.

2. BreadcrumbList — cheap and broadly eligible

The article’s priority #2. Chaque item in the liste is un crumb; position doit be sequential starting at 1.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "BreadcrumbList",
  "itemListElement": [
    {
      "@type": "ListItem",
      "position": 1,
      "name": "Home",
      "item": "https://www.example.com/"
    },
    {
      "@type": "ListItem",
      "position": 2,
      "name": "Blog",
      "item": "https://www.example.com/blog/"
    },
    {
      "@type": "ListItem",
      "position": 3,
      "name": "Schema Markup",
      "item": "https://www.example.com/blog/schema-markup/"
    }
  ]
}
</script>

• The dernier crumb (the current page) peut omit item per Google’s docs, but notamment it doesn’t hurt — Google ignores it pour the final entry.
• Getting position out of sequence, or skipping a level, is the la plupart courant raison ce type fails the Résultats enrichis Tester.

3. Product — the highest-value ecommerce type

The article noms Product as the type with price, availability, and examiner ratings — “the highest-value type for ecommerce.” A minimal eligible exemple:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Wireless Trail Headphones",
  "image": "https://www.example.com/images/headphones.jpg",
  "offers": {
    "@type": "Offer",
    "priceCurrency": "USD",
    "price": "89.99",
    "availability": "https://schema.org/InStock"
  },
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.6",
    "reviewCount": "212"
  }
}
</script>

• offers and its price/availability are ce que rend the listing eligible pour the price and stock-status afficher the beginner lens describes.
• aggregateRating doit reflect ratings que en réalité exist on lune page — the article’s “only mark up content that’s actually on the page” rule s’applique directement ici; an invented rating count is exactly the kind of markup Google’s guidelines prohibit.

4. Article — pour editorial content

The article’s priority #4, pour the blog/news content ce lens system itself runs on.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "BlogPosting",
  "headline": "Schema Markup: Win Rich Results and Feed AI Search",
  "datePublished": "2026-06-26",
  "dateModified": "2026-07-13",
  "author": {
    "@type": "Person",
    "name": "Jane Author",
    "sameAs": "https://www.linkedin.com/in/jane-author"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Acme Consulting",
    "logo": {
      "@type": "ImageObject",
      "url": "https://www.example.com/images/logo.png"
    }
  }
}
</script>

• author.sameAs fait the même entity-disambiguation fonctionner as Organization’s sameAs — it’s the Person version of the même lever.
• dateModified devrait track réel edit dates, the même discipline ce site s’applique to its propre updated frontmatter — don’t bump it sans a réel modifier.

Broken vs. fixed: the sameAs mistake

The Avancé lens warns que a sameAs pointing at the incorrect profile peut causer an engine to conflate unrelated entities. Here’s ce que que semble comme in pratique.

Broken — sameAs points at a same-named but unrelated company:

{
  "@type": "Organization",
  "name": "Acme Consulting",
  "sameAs": ["https://en.wikipedia.org/wiki/Acme_Corporation"]
}

Acme Corporation is the Looney Tunes prop company’s Wikipediune page, pas ce business — a réel risk whenever a brand nom is courant or generic.

Fixed — sameAs verified to resolve to the réel entity being décrit:

{
  "@type": "Organization",
  "name": "Acme Consulting",
  "sameAs": ["https://en.wikipedia.org/wiki/Acme_Consulting_(software_company)"]
}

Avant shipping quelconque sameAs valeur, ouvrir the lien yourself and confirmer it describes the même organization, person, or brand votre markup noms — the même scrutiny the article recommends giving a balise canonical.

Testez vos connaissances: Balisage de données structurées

Five rapide questions on balisage de données structurées, the schema.org vocabulary, and ce que it fait (and doesn’t) do. Pick an réponse pour chaque, alors vérifier.

Ressources utiles

My balisage de données structurées writing

• Balisage de données structurées pour AI — the AI-search angle: pourquoi schema isn’t a direct citation lever but is entity infrastructure (a différent question from ce article).
• The Beginner’s Guide to SEO technique — où I frame schema as code que helps engines comprendre content and “powers many features that can help your website stand out.”
• Enterprise SEO — my pragmatic prendre: “I’m a fan of schema markup as long as it gets you a search feature.”
• On-Page SEO Checklist — schema as code que “helps search engines understand the information on a page” and powers rich snippets que peut earn plus clicks.
• Ce que Is Balisage de données structurées? How to Ajouter It & Pourquoi c’est important — Ahrefs’ dedicated guide to the principal types and implementation.

From autour the industry

• Google retires 7 données structurées fonctionnalités (June 2025) — coverage of the Book Actions / Claim Examiner / Estimated Salary / Learning Video / Special Announcement / Vehicle Listing / old Course Info removals.
• Google drops FAQ résultats enrichis from search (Moteur de recherche Journal) — the FAQ deprecation and Ce que cela signifie pour sites que relied on it.
• Balisage de données structurées and AI search: aucun hype (Moteur de recherche Land) — a grounded regarder at ce que schema fait and doesn’t do pour AI search, with the Fabrice Canel context.
• Is balisage de données structurées a ranking factor? (Moteur de recherche Journal) — the rep statements and evidence behind “not a direct ranking factor.”
• John Mueller on the 2026 schema mettre à jour — “Google is not killing schema… markup types come and go.”
• schema.org documentation — the vocabulary itself, straight from the source.

Journal des modifications