Examples of Structured Data

Have you ever looked up a movie rating, and at the top of the page you quickly find ratings from three different websites — say, Rotten Tomatoes, IMBd, and Common Sense Media — neatly placed at the top of the Google search results page? Do wonder how Google picks out this information?

Information that appears right on the search results page — like reviews, recipes, and images — are called rich results, and they are powered by structured data. Webmasters use structured data to organize information on their site, so that search engines can better determine the content of their site and answer user search queries more effectively. In this article, you’ll learn an overview of the three structured data formats that are compatible with the Google search engine, and we’ll provide examples of what they look like.

For a basic introduction to how structured data works, why it’s needed, and what the benefits are, check out our Structured Data Guide.  

Table of Contents

  • What is structured data?
  • structured vs unstructured data
  • unstructured data
  • Three types of structured data used by Google
  • Structured Data Examples: RDFa, Microdata, and JSON-LD
  • RDFa structured data markup + Example
  • Microdata structured data markup + Example
  • JSON-LD Structured Data Markup + Example
  • Benefits of JSON-LD Schema

What is Structured Data?

Structured data is a standardized format for providing information about a page and classifying that content on the page; for example, on a recipe page, what are the ingredients, the cooking time, the temperature, the calories, and so on.

— Google Developers

Structured data makes it possible for search engines to understand content across the internet and, as a result, deliver a better user experience and make it easier for people to find things that they are looking for when they use a search engine to find information on the web.

Google is continually building a more semantic web, which means these markups are increasingly valuable in internet communication.

What do we mean when by semantic web? The Semantic Web is essentially an extension of the current web, where information is given a clearly-defined meaning that both computers and people can understand. This enables computers and people to more effectively work together.

Structured vs. Unstructured Data

Structured data is created using predefined schema and is organized in a clear, tabular format. Similar to a table, the data is defined by a clear structure where each line of data corresponds with a specified value.

Most of the internet is unstructured data. Data is considered unstructured when it doesn’t conform to a predefined tabular format with clear input categories.

Unstructured data:

  • social media
  • text files
  • email
  • Images

Schema is a structured data vocabulary that works to define unstructured data (entities, actions, and relationships) like the items listed above so that search engines like Google can understand them. serves as a sort of like a library of this vocabulary and most Search structured data relies on it. does provide helpful information about structure data implementation and other documentation, but it’s important to also consult Google’s documentation on structured data or the documentation of whichever search engine you would like to rank in. 

As for examples of structured data, we’ll focus on the types of structured data that are relevant to search engines.

The three types of structured data used by Google:

Structured Data Examples: RDFa, Microdata, and JSON-LD

RDFa, microdata, and JSON-LD are all code languages that can be embedded into HTML to include metadata on a web document. 

RDFa structured data markup: 

RDFa stands for Resource Descriptive Framework in Attributes. RDFa was created to bridge the gap between human-oriented HTML and machine-oriented RDF documents. It uses the RDF data model and semantic web vocabularies directly. When we say *semantic web vocabularies, we’re talking about the language used to describe a set of data, including the semantic correlation between words.

RDFa structured data can be added to any HTML, XHTML, or XML-based document. RDFa helps a search engine understand the user-facing content of a page through HTML tag attributes. RDFa is commonly used in the head and body sections of an HTML page.

Example of RDFa:

Here’s an example of some contact information listed on a website for Amy Rogers. This is what it looks like on the user-facing side:

Amy Rogers Founder/CEOPhone: (540) 961-4469

E-mail: [email protected]

Links: Amy’s Homepage

Then the backend, with RDFa will look like this:

Image: RDFa structured data example

Microdata structured data markup: 

Another way to implement schema into your HTML is through a microdata format. This format works by implementing “item scope”, “item type” and other item distinctions and then inserting the vocabulary that correlates to the item that’s in the page’s content. For example, if you’re trying to tell a search engine that “The Shining” by Stephen King is on your web page, then you would find the schema vocabulary for “Books” and insert that into your microdata structure. 

Example of Microdata:

The front end: The Shining, by Steph King.


Image: Microdata structured data example

JSON-LD Structured Data Markup

As previously mentioned, JSON-LD is Google’s preferred format. It is the golden standard for structured data markup. You’ll want to include your JSON-LD script in the header, or near the top of the page.

Compared to microdata, JSON-LD is easier to implement, because you can simply paste the markup within the HTML document, versus having to wrap the markup around HTML elements. JSON-LD will help a search engine establish facts surrounding entities (items on the web.)

Examples of JSON-LD:

JSON-LD markup will always begin with the following line: