What is JSON-LD?

JSON-LD is a JSON-based serialization format for Linked Data, data connected according to the RDF model, standardized by the W3C as a Recommendation. The difference from a plain JSON document comes down to three keys: @context, which maps short property names to public vocabulary URIs (typically schema.org); @id, which declares that a value is a reference to a resource rather than a plain literal; and @type, which classifies an entity using a vocabulary term, for example "Product" or "FAQPage". The most concrete advantage is that the file stays plain JSON: any parser or pipeline already in use keeps working, while the extra meaning lives inside @context. It is a W3C standard (JSON-LD 1.1), not an informal convention, which is why search engines and analysis tools interpret it natively. Newer sibling serializations also exist, such as YAML-LD for hand-editable metadata and CBOR-LD, a binary, compressed variant for bandwidth-constrained channels.

The use case everyone already knows: structured data for search

The most concrete entry point for a business is schema.org structured data markup: an online store publishing JSON-LD of type "Product" with price and availability, an article marking its frequently asked questions as "FAQPage", an event declaring its date and location as "Event". Search engines read this block and use it to generate rich results, the star ratings, prices or accordions visible directly on the results page. The same mechanism applies to AI assistants and AI Overviews: a fact marked with a clear @type and @id is far easier to extract and cite correctly than the same fact buried in non-semantic HTML, where the system has to guess what a number or a label actually means.

What JSON-LD is NOT

JSON-LD is not an ontology: an ontology formally defines the concepts, relationships and rules of a domain, while JSON-LD is only the syntax used to write those concepts into a document, often referencing a lightweight ontology such as schema.org inside @context. It is not a knowledge graph either: a knowledge graph is the network of entities and relationships an organization builds and queries, and JSON-LD is one format that linked data can be published or exchanged in, not the structure itself. And it does not solve entity resolution: @id gives an entity a stable identifier, which helps recognize it across systems, but matching records that refer to the same real-world entity remains a separate discipline. Anyone looking for the RDF extension that annotates a single triple with provenance or confidence should look at RDF-star instead, and a JSON-LD-star variant does exist for that, but it is a side note to JSON-LD itself.

An enterprise example

A company selling products, running events or publishing an FAQ page can mark up each of these with JSON-LD instead of leaving a search engine or an AI assistant to interpret unstructured HTML to infer a price, a date or an answer. The result is that those facts become directly extractable and citable, reducing the risk that an external system reports something wrong because it reconstructed it by guesswork.

Why it matters for decision makers

Structured data is one of the few concrete, low-effort levers for visibility in generative search: a model generating an answer or an AI Overview can extract and cite a fact marked with JSON-LD far more reliably than it can by reading free text. For an organization that wants to be cited by AI assistants as well as indexed by traditional search engines, marking up key content with JSON-LD is a minimal technical investment with a direct return on digital visibility.