JSON-LD
A W3C-standard syntax that places schema.org vocabulary inside a <script type="application/ld+json"> block separate from HTML body — Google's preferred structured data format
What is JSON-LD?
JSON-LD (JSON for Linked Data) is a W3C-standard syntax for writing schema.org vocabulary as JSON inside a <script type="application/ld+json"> block, separate from the HTML body. It is one of three structured-data syntaxes specified by schema.org (alongside Microdata and RDFa), and Google Search Central's guide explicitly recommends JSON-LD over the alternatives.
Why is JSON-LD Preferred?
| Syntax | Location | Extraction Robustness | Google Recommendation |
|---|---|---|---|
| JSON-LD | <script> block (separate from HTML body) |
✅ Unaffected by body markup changes | ✅ Preferred |
| Microdata | HTML attributes (itemscope / itemtype) |
△ Bound to HTML parse tree | Compatible |
| RDFa | HTML attributes (vocab / typeof) |
△ Bound to HTML parse tree | Compatible |
Two reasons:
- Separation from HTML body — JSON-LD is written as standalone JSON inside
<script>blocks. HTML markup changes don't break schema data. - Extraction robustness — Microdata and RDFa depend on the HTML parse tree, so some bots may fail to extract them correctly. JSON-LD's tokenization is consistent across consumers.
JSON-LD 1.1 also holds the highest official standard status as a W3C Recommendation (2020-07).
JSON-LD from a GEO / AEO Perspective
JSON-LD-first extraction patterns are observed in real LLM citation logs across ChatGPT, Claude, Gemini, and Perplexity answers. When an AI answer identifies a page as an entity and decides whether to cite it, JSON-LD is the most reliable structured-data format.
In particular, the Cite-source strategy from Aggarwal et al. KDD 2024 provides academic backing: declaring sources via schema.org fields like Article.citation and Organization.url in JSON-LD outperforms writing "Source: ..." inline in body prose for LLM extraction robustness.
Implementation Example
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Article",
"headline": "RanketAI Guide #06: Schema.org 13 Types and Their GEO Impact",
"author": { "@type": "Organization", "name": "RanketAI Editorial" },
"datePublished": "2026-05-09",
"publisher": {
"@type": "Organization",
"name": "RanketAI"
}
}
</script>