Structured data tells search engines what your content means, not just what it says. When implementing schema markup for Knowledge Graph optimization, you face a fundamental choice between formats: JSON-LD, Microdata, or RDFa. In 2026, one option has emerged as the clear standard.
This guide compares JSON-LD vs Microdata, explains why Google explicitly recommends JSON-LD, and helps you choose the right format for Knowledge Graph success.
Schema.org vocabulary can be implemented in three different syntaxes. Each conveys the same semantic information to search engines, but they differ dramatically in implementation approach.
JSON-LD places structured data in a separate script block within your HTML, completely decoupled from visible content:
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Company Name",
"url": "https://example.com"
}
</script>
The markup lives independently from your page's HTML structure. Content changes don't affect schema, and schema changes don't affect content.
Microdata embeds schema attributes directly into HTML elements using itemscope, itemtype, and itemprop attributes:
<div itemscope itemtype="https://schema.org/Organization">
<span itemprop="name">Company Name</span>
<a itemprop="url" href="https://example.com">Website</a>
</div>
Every schema property requires modifying HTML elements that display the corresponding content.
RDFa uses a different attribute syntax but shares Microdata's approach of embedding within HTML:
<div vocab="https://schema.org/" typeof="Organization">
<span property="name">Company Name</span>
<a property="url" href="https://example.com">Website</a>
</div>
RDFa offers more flexibility than Microdata but adds complexity most implementations don't need.
Google's documentation explicitly states: "Google recommends using JSON-LD for structured data whenever possible." This isn't arbitrary preference—it reflects practical advantages that matter for Knowledge Graph optimization.
JSON-LD keeps structured data separate from presentation markup. When designers change page layouts, schema remains intact. When developers update schema, they don't risk breaking visual elements. This separation reduces errors and simplifies maintenance.
Adding JSON-LD requires inserting a script block—no modification of existing HTML required. You can update schema properties without touching any other code. For sites using content management systems, JSON-LD can be generated dynamically and injected without altering templates.
JSON-LD works seamlessly with Google Tag Manager and similar tools. You can deploy, modify, and test structured data without developer involvement or code deployments. This flexibility accelerates implementation and iteration.
Microdata fails when content gets removed or restructured. If your "Company Name" span disappears during a redesign, your Organization schema breaks. JSON-LD remains valid regardless of what happens to visible content—as long as the script block exists, the schema works.
Nested schemas—Person within Organization within Article—become unwieldy with Microdata. The HTML structure must accommodate schema nesting, often requiring wrapper elements that serve no visual purpose. JSON-LD handles nesting naturally through object properties.
Despite JSON-LD dominance, Microdata retains limited use cases.
Some older platforms generate Microdata automatically and lack JSON-LD support. Converting established Microdata implementations offers diminishing returns if the existing markup validates correctly and serves its purpose.
If schema values must always match visible content exactly, Microdata's coupling ensures synchronization. When the displayed company name changes, the schema changes automatically. This prevents drift between visible content and structured data—though it introduces the fragility mentioned earlier.
Hand-coded static sites with minimal schema needs can implement Microdata without significant maintenance burden. If you're marking up a single page with basic Organization schema that won't change, format choice matters less.
Choosing JSON-LD is step one. Implementing it effectively requires attention to several factors.
Place JSON-LD in the <head> section when possible. This ensures search engine crawlers encounter structured data early in page parsing. Multiple JSON-LD blocks are valid if you need to separate different schema types.
The @id property creates referenceable entities that connect across schema blocks and pages:
{
"@context": "https://schema.org",
"@type": "Person",
"@id": "https://example.com/#founder",
"name": "Jane Smith"
}
Other schemas can reference this entity by @id, building the relationship network that Knowledge Graph recognition requires.
Connect your entities to authoritative external sources. sameAs links to Wikipedia, Wikidata, LinkedIn, and industry directories help Google verify entity existence and attributes:
"sameAs": [
"https://linkedin.com/in/janesmith",
"https://wikidata.org/wiki/Q12345678"
]
Test JSON-LD with Google's Rich Results Test and Schema.org's validator. Syntax errors prevent search engines from parsing your structured data entirely. Validation catches issues before they affect Knowledge Graph indexing.
If you're currently using Microdata, migration to JSON-LD offers long-term benefits.
Document all schema types and properties currently implemented. Use browser developer tools or structured data testing tools to inventory what exists.
Build JSON-LD blocks containing the same entities and properties. Test thoroughly to ensure equivalence before deployment.
Add JSON-LD to pages while keeping Microdata in place. Google handles duplicate markup gracefully, using whichever version it parses successfully. This approach allows testing without risk.
Once Google Search Console shows successful structured data parsing from JSON-LD, remove Microdata attributes from HTML. Monitor for any validation warnings after removal.
| Factor | JSON-LD | Microdata |
|---|---|---|
| Google recommendation | Preferred | Supported |
| Implementation location | Separate script block | Within HTML elements |
| Maintenance difficulty | Low | High |
| Layout change resilience | High | Low |
| Tag Manager compatible | Yes | No |
| Complex schema handling | Excellent | Difficult |
| Content synchronization | Manual | Automatic |
The format choice extends beyond traditional search. AI systems like ChatGPT, Perplexity, and Google's AI Overviews reference Knowledge Graph data when generating responses. JSON-LD's clean, parseable structure makes entity information more accessible to these systems.
When AI platforms crawl and process web content, JSON-LD provides unambiguous structured data separate from potentially confusing HTML markup. The same advantages that make JSON-LD easier for Google to parse apply to AI systems building their understanding of entities and relationships.
Investing in proper JSON-LD implementation positions your structured data for both current Knowledge Graph recognition and emerging AI citation patterns.
For Knowledge Graph optimization in 2026, JSON-LD is the clear choice. Google recommends it, implementation is simpler, maintenance is easier, and the format handles complex schemas that entity recognition requires.
Microdata isn't wrong—search engines still parse it successfully. But the practical advantages of JSON-LD make it the standard for new implementations. If you're building structured data for Knowledge Graph presence, start with JSON-LD.
The question isn't really which format search engines prefer—they understand both. The question is which format lets you implement, maintain, and iterate on structured data effectively. JSON-LD wins that comparison decisively.
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