Schema.org: what it is and how to use markups and structured data
It basically is “the” standard that enables the use of structured data to improve the interpretation of content by search engines and is the backbone of the semantic web. Founded in 2011 by Google, Microsoft, Yahoo, and Yandex, the Schema.org project has continued to evolve and, to date, more than 45 million web domains use more than 450 billion objects, embedded on web pages, emails, and more, and leading digital platforms are making use of this vocabulary to power richer, more interactive experiences. Through markup such as Microdata, RDFa , and JSON-LD, Schema allows us to add semantic “labels” to our pages, thereby improving search engines’ crawling and understanding of content: this not only helps optimize results on Google, but also facilitates the creation of rich snippets, extractions from Knowledge Graph and other advanced SEO features, and multimedia results that can enrich the browsing experience for users. In this guide we will explore what Schema.org is, how it works, and why its implementation is essential to boosting a website’s visibility.
What is Schema.org
Schema.org is the standard vocabulary used to structure the data on a web page and improve understanding by search engines. It refers specifically to the set of types and structures for describing entities and semantic relationships on the Web, which forms a body of information actually used in markup on Web pages, as a standard for structured data.
The Schema.org project was created in 2011 through the collaboration of Google, Bing, Yahoo and Yandex specifically to standardize the way structured data is read by search engines. It is thus a collaborative platform created by the major search engine companies with the mission of providing a shared vocabulary for structured data that would allow webmasters to semantically mark up the content of web pages in a uniform and codified manner, making that information more easily understood by search engine crawlers.
From a technical point of view, the Schema.org vocabulary is based on a set of enumerated types, properties and values that, by means of appropriate markup, allow the entities, relationships and meanings of content on pages to be precisely described, reducing ambiguity and improving comprehension by web machines. Not surprisingly, more than 45 million domains now have 450 billion Schema.org objects embedded within them, demonstrating the scope and relevance of this technology.
This massive vocabulary supports various structured data serialization technologies, including JSON-LD, RDFa and Microdata, and has actually succeeded in developing a semantics that makes data usable and readable not only by human users, but also (and especially) by search engine spiders.
In concrete terms, the result is increased relevance and visibility of information presented in SERPs, such as the generation of rich snippets or other advanced features that improve the quality of results shown on queries.
What is a markup and what it is used for
To understand what schema.org is, we need to take a step back and also provide a definition of markup, which as mentioned is one of the basic elements of this project.
At a general level, the term markup refers to a markup language that uses tags or markers to identify and represent the structure and characteristics of content in a document, imparting semantic and structural meaning . Markup languages annotate text with structural elements, such as headings, paragraphs, images or links, providing specific interpretations of how this information is to be displayed or processed.
The best-known markup language is HTML (Hypertext Markup Language), used to structure the content of Web pages, such as headings, paragraphs, images and hyperlinks, but XML (Extensible Markup Language) and Markdown are also examples.
In the context of Schema.org , markup refers to the set of semantic tags defined by Schema.org and used within an HTML document to describe the content of that page in a structured way.
Unlike plain HTML, which deals primarily with visualization, Schema.org markup adds a level of semantic meaning to data through the use of predefined schemas (or conceptual models), such as Product, Event, Recipe, Person and so on. Schema.org semantic markup is implemented through formats such as JSON-LD, Microdata or RDFa, embedded within the HTML code of a page. These markups reduce the ambiguity of data on the Web by providing precise information about the entities present, such as a product with name, price and availability, or a person with name, biography and affiliation.
For example, here is a JSON-LD implementation of Schema.org markup for an Event:
<script type=”application/ld+json”>
{
“@context”: “https://schema.org”,
“@type”: “Event”,
“name”: “Classical music concert”,
“startDate”: “2025-07-19T20:00”,
“location”: {
“@type”: “Place”,
“name”: “Teatro Italia”,
“address”: {
“@type”: “PostalAddress”,
“streetAddress”: “Via Dante 15”,
“addressLocality”: “Milano”,
“postalCode”: “20121”,
“addressCountry”: “IT”
}
},
“offers”: {
“@type”: “Offer”,
“url”: “https://www.example.com/concert_tickets”,
“price”: “30.00”,
“priceCurrency”: “EUR”
}
}
</script>
In this case, JSON-LD markup is used to describe a music event. The structured data specifies information such as the event name, start date, venue, and ticket details, which can be read by search engines to build a richer representation of this entity in the SERP.
What is microdata
To summarize, then:
- Schema.org is a coordinated vocabulary for structured data that improves search engine understanding of content.
- Markup is a markup system for annotating the content of a document with structural and semantic information.
- Schema.org markup is a specific form of semantic markup that allows entities and relationships to be described through terms accepted by search engines, improving indexing and visibility of content in SERPs.
The Schema language is therefore based on tags or microdata that we can add to the HTML of our pages to improve the way search engines read and represent the page in SERPs.
More specifically, schema markup or markup is a form of microdata used to represent data; the actual data is called structured data and organizes the content of the page making it easier for Google and other crawlers to understand the information. When added to a Web page, schema markup is read by search engines, which more precisely recognize the meaning and relationships behind the entities and can provide multimedia or rich snippet results in search results to enhance the user experience.
The essential element is therefore microdata, an HTML5 specification used to nest metadata within existing content on Web pages: search engines, Web crawlers and browsers can extract and process microdata from a Web page and use it to provide a richer browsing experience for users.
As a result of the use of microdata, the structure of the Web site will be simple and easy for search engines to scan, and this can improve its visibility in search results pages, as well as potentially affecting ranking as well.
The history and evolution of Schema.org
Schema.org was officially born on June 2, 2011 from a crucial collaboration between some of the major players on the web, namely Google, Microsoft (Bing) and Yahoo (joined a few months later by Yandex, the main search engine in Russia) with the intention of providing a common and shared standard that could facilitate the semantic interpretation of content on web pages and, more generally, on the Internet, in e-mail messages and beyond, repeating a sharing operation already experimented a few years earlier with the development of XML sitemaps. The project immediately established itself as a benchmark for data structuring, playing a strategic role in the growth of semantic SEO and helping to make machine-readable data easier and more accessible for millions of sites . In particular, it has progressively helped standardize the HTML tags used on sites to create multimedia results on specific topics and the creation of a single foundation for the information that powers applications used every day by users around the world.
Since its inception, the project has attracted substantial support from the international community, which through active participation on platforms such as GitHub and the W3C’s dedicated mailing list has helped to continuously improve the vocabulary and related schemas. This open collaboration ensured that Schema.org evolved in parallel with the rapid growth of the Web, adapting to new technological trends and updating its tools for gradually emerging needs.
In fact, the platform was designed to allow for continuous expansion and adaptation. Since its launch, Schema.org has experienced continuous growth, with new versions and regular releases, built around an incremental development model. Each version is associated with improvements in functionality, new data types, properties, and bug fixes. Among the most notable updates we can mention some key evolutions, such as the introduction of JSON-LD in 2015, which revolutionized the way structured data is represented, making its implementation simpler and more transparent. Other historical releases, such as those related to the timely response to the Coronavirus emergency, with the introduction of specific types such as SpecialAnnouncement and CovidTestingFacility, showed how responsive the project was to global events and real-world contingencies.
From the earliest stages, the project evolved to meet the needs of a plurality of fields, creating an environment where experts from different fields could contribute through specific extensions and integrations. In 2015, to further facilitate this collaborative approach, hosted extensions were introduced, extensions that allow new schemas to be added while maintaining tight integration with the core vocabulary. These extensions, such as bib.schema.org (for bibliographies) or auto .schema.org (for automotive), require discussion and coordination within the community before officially integrating into the core. In parallel, external extensions have also been developed, similar in operation but more decentralized, allowing experts in certain fields to extend vocabularies without duplicating terms or resources.
In short, in addition to the established contributions of the core players Schema.org has expanded through strategic collaborations with external projects that have enriched the vocabulary and contributed to an open development model. One of the first projects to be incorporated was GoodRelations, an initiative that greatly improved support for eCommerce and business situations by expanding capabilities in representing terms related to products, offers, prices, and payment terms. Another major contribution came from LRMI, the result of a collaboration with Creative Commons and the Association of Educational Publishers, which introduced dedicated metadata for educational resources used by schools, institutes, and learning portals to harmonize descriptions and make it easier to find this information through search engines.
One of the great strengths of Schema.org lies precisely in its dynamic approach to development. Among the latest releases, in addition to the official published versions, we also have “staging” releases, accessible at staging.schema.org, which reflect ongoing developments in real time and represent a kind of preview for future updates. Produced by ongoing community discussion, these releases are first vetted by the Project Steering Group, the project’s oversight committee, and then formally approved if no objections are raised within a review period.
Not just schema: the other vocabularies of the semantic web
The Semantic Web is not a new concept nor is it limited to the Schema.org experience. Before and concurrently with its emergence, other vocabularies attempted to fill the need for structuring content, but each found different specific areas of application.
As early as the mid-2000s, in particular, vocabularies such as FOAF (Friend of a Friend) and Dublin Core developed standards aimed at particular domains:
- FOAF, which had long been used to represent individuals and their social relationships, was particularly popular in the area of describing online identities and social networks.
- Dublin Core, on the other hand, has emerged as one of the leading standards for bibliographic description of resources in contexts related to libraries, digital archives, and scientific repos. Its metadata set has found wide application in academia and digital content.
In parallel, the Linked Data project has gained ground with the goal of interconnecting structured data on the Web through the use of RDF (Resource Description Framework) and custom ontologies. However, these technologies-while powerful and powerful in terms of interoperability-have often required more sophistication, limiting their adoption to areas with particularly complex or regulated information management , such as government institutions or life sciences.
It was starting in 2011, with the birth of Schema.org, that the real breakthrough for semantic SEO occurred : unlike FOAF or Linked Data, Schema.org was already born with the clear mission of reaching the general public and simplifying the adoption of structured data on the web, without requiring complex semantic frameworks. Moreover, the project did not exclude previous approaches but, as in the aforementioned case of GoodRelations, enhanced them in a more search engine optimization-oriented context .
Schema.org and SEO: the utility and benefits
Structured data and thus the concept behind schema.org represent the foundational element of modern SEO, articulating itself as a kind of markup language that provides meaning to web content beyond simple text strings.
Through the use of this vocabulary, data is organized in a coherent manner, allowing search engines to go beyond the mere literal interpretation of content to understand its true meaning and context. From a utilitarian perspective , by adding this simple code to our pages we can provide vital information to search engines, which can improve the effectiveness of the results shown, online visibility as well as the click-through rates of our site and pages. Most importantly, microdata can provide context to an otherwise ambiguous Web page.
Using microdata, RDFa or JSON-LD we are able to precisely mark up specific entities such as products, organizations, reviews, events, people and many, many more. Not only does this semantic enrichment help Google and other search engines better understand what is on a page, but it allows them to return much more targeted and detailed results to users. As we know, Google (and other search engines) use such microdata to compose the features of their SERPs, and here for example are listed some of the main types of multimedia results linked to structured data that appear on Google.
On the SEO side, schema.org markup has become increasingly important in recent years, to the point of being considered one of the ranking factors for Google; but it is also a key element in the creation of the Knowledge Graph, a skeleton that allows the definition of structured data and, last but not least, a factor to be exploited for voice searches. In practice, Google’s evolutions draw fully on this form of microdata.
How semantic markups improve SEO
The implementation of structured data also enables other improvements in terms of SEO.
First, by making it easier for spiders to read content, we facilitate the indexing process , allowing engines to more easily discover the critical information you want to communicate. Second, we increase the possibility of getting rich snippets or rich results, those portions of enriched content that emerge in Google’s SERPs and offer a significant advantage in terms of click-through rate (CTR). For example, a product review page may be more visible due to details such as star ratings or the presence of user reviews directly in search results.
Confirming this, numerous studies and research show that pages enriched with structured data tend to perform better in SERPs precisely because they are able to respond with greater precision and detail to people’s search intent and direct needs.
Schema and Google: how structured data powers Rich Result and Knowledge Graph
Looking at how Google Search works, implementing structured data with Schema.org can therefore support the broader goal of gaining advanced visibility in SERPs, primarily because it allows site content to be identified and represented in an enriched form through so-called Rich Results. At the same time, Schema.org also powers Google’s Knowledge Graph , the information network that enriches the search experience by answering specific queries with fact sheets that go beyond simple links to external sites.
In short, Rich Results are enriched versions of traditional search results that include details extracted directly from web pages through the use of structured data. These results can come in many forms, such as rich snippets, carousels, recipes or reviews, all of which greatly enhance theuser experience and increase the likelihood that a site will be clicked on.
Through theimplementation of major schema such as Product, Review, Event or Recipe we provide Google with granular information in the form of microdata, which allows the search engine not only to understand what a page contains, but also to compose featured snippets, FAQ expanded rich results or previews in Google Discover. Using Schema.org on key pages significantly expands the chances that a Rich Result will be displayed that will attract the user with visual or textual content of immediate interest.
A typical example of how this works is the implementation of Schema Product in the context of an eCommerce site. Incorporating structured data on pricing, availability and reviews can result in Google directly representing these features in SERPs, incentivizing users to click on the link for more detailed news.
In addition, Schema.org occupies a central position in the construction of the Google Knowledge Graph, the powerful tool that collects and organizes information to more comprehensively answer user queries. The Knowledge Graph is based on a network of interconnected entities and can return direct answers through information panels, reducing the need for users to click on external results to get the information they need.
Through structured data we provide search platforms with the entities and relationships needed to populate the Knowledge Graph. For example, a schema for a person or organization not only states that a page is about an individual or entity, but specifies who they are and what attributes or relationships they have with other elements. This deep level of data helps Google build a detailed profile in the Knowledge Graph, which in turn is called out in immediate responses through the SERP, for example with the Knowledge Panel.
A classic case is that of celebrities or public figures: when Web pages associated with them properly use schema such as Person, Organization or CreativeWork, they help aggregate the information that we then see in the summary panels in the right column of the SERP.
Schema.org and the Semantic Web
The standardization offered by Schema.org has thus fundamentally changed the way we think about and build our SEO. It is no longer just a matter of optimizing keywords or acquiring backlinks: today to be truly competitive you need to enrich your site with semantic markup, making sure that search engines fully understand the context and concepts we want to communicate. This allows us not only to improve visibility and ranking, but also to offer users more precise, relevant and useful answers.
The effects of this approach thus go beyond old SEO tactics and help make web content more consistent, accessible and meaningful even for emerging technologies, such as voice search or virtual assistants. In the context of modern SEO, the integration of semantic markup, ably supported by Schema.org, is now considered an essential practice for those who wish to enable clear and extensive communication between their website and the machines that parse it.
Here, then, Schema.org does more than just improve a site’s SEO: it is a key to anticipating and preparing for future evolutions in online search, holding strategic importance in any digital project that aspires to compete for visibility and relevance in today’s scenario.
Schema.org ‘s greatest strength lies in its ability to facilitate the evolution of the Web toward an increasingly intelligent and contextual form , based on the principles of semantic SEO. Indeed, the Semantic Web differs from the traditional Web in its ability to understand not only the data, but the relationships between them, interpreting their meaning accurately. Schema.org is one of the keys to this crucial shift, offering webmasters a tool with which to label their pages semantically , providing search engines with clearer and more specific elements for interpretation.
When a search engine such as Google scans the web, using the structured data offered by Schema.org helps give more context to concepts such as products, reviews, people, events, and more that represent the content on the page. Compared to the past, where engines were limited to a “superficial” reading of text, today they are enabled tosemantic interpretation, accurately identifying entities and the relationships between them. This leads to an improvement in the user experience not only in terms of more relevant search results, but with rich interfaces and informative sidebars that populate tools such as the Knowledge Graph .
But the importance of Schema.org does not stop there. Its applications extend to the modernization of SEO optimization techniques based on machine learning algorithms and the integration of new technologies such as voice assistants. Indeed, the structured data interpreted by Schema.org contributes to the operation of emerging tools such as Google Assistant or Amazon Alexa, which rely on intelligent context interpretation to respond to users’ voice queries. In this way, the vocabulary contributes to making web pages “understandable” not only to crawlers, but also to these advanced technologies that are changing the very way online information is enjoyed.
Why use schema.org markup on a site?
Markup began to become particularly useful in the era of Hummingbird and RankBrain: the way a crawler, and Googlebot specifically, interpreted the context of a query determined the quality of a search result, and the right use of Schema.org could provide context to an otherwise ambiguous Web page.
Even today, the various types of structuring help refine content more clearly or more prominently in search results, and so potentially every site and every online activity can benefit from implementing Schema.org, to generate rich snippets or even simply to offer specific, targeted information to users, especially for local searches.
On the other hand, this stems from the very nature of the project, developed as mentioned with the ambitious idea of improving the web by creating a structured data markup schema supported by the major search engines Google, Bing, Yandex, and Yahoo! to create advanced search functionality for users and enable them to find more relevant information in SERPs. Through on-page markup, search engine crawlers understand Web page information better and more easily and provide richer search results. A shared vocabulary, as mentioned above, also allows webmasters to have functional, working reference templates to maximize work and business results for their efforts.
How Schema.org works: taxonomy and vocabulary structure
Having completed this theoretical overview, let us turn to the more practical aspects of this topic.
Schema.org provides a hierarchical vocabulary for describing entities and relationships using structured data. This vocabulary consists of types or classes and a set of properties that allow a wide range of concepts to be categorized, such as products, people, events, organizations,ì and more.
Currently, as of the most recent update in September 2024 Schema.org includes:
- 803 types (types), representing different concepts and entities
- 1461 properties (properties), assigned to each type to detail characteristics
- 14 data types (DataTypes), which describe values such as text strings, dates, or numbers
- 87 enumerations (properties that accept default values)
- 467 enumeration members, which represent the possible options for each enumeration.
The hierarchical structure of the vocabulary allows concepts to be organized into progressive levels, starting from generic types , such as the type “Thing”, toward more specific classes. Thing is the highest level type that refers to “anything” and is commonly used as the basis for hundreds of Web domains. From here more specific types branch out, such as Person, Product, Event, Organization and so on. Each type has its own set of properties to describe the specific characteristics of that particular entity.
For example, for the Product type, properties such as name, brand, and offers allow a product to be described in detail, including information such as name, brand, and associated offers. Similarly, the Person type describes information such as name, job title and affiliations. Through this hierarchical and logical modeling, Schema.org allows search engines easy extrapolation of information on websites, improving the identification of relationships between different entities.
What are the main Schema.org markup formats.
One of the pillars of Schema.org ‘s effectiveness is its flexibility in adapting to different formats for use. Because of this versatility, structured data can be implemented in various ways, facilitating integration with different platforms and technologies, and adapting to the technical preferences of developers and webmasters.
As mentioned, there are three main formats that Schema.org supports: Microdata, RDFa and JSON-LD.
Microdata was among the first formats adopted by Schema.org when it was launched. Implemented directly in the HTML code of pages, the markup is added to existing elements with a simple HTML5 syntax. Although it was among the original formats, its use is declining, mainly because it requires direct changes to HTML, which can complicate management, especially for those using content management systems. However, it remains widely supported by search engines such as Google, which interprets its data correctly.
The most recent and widely recommended alternative by search engines, including Google, is the JSON-LD (JavaScript Object Notation for Linked Data) format. Introduced in 2015 and quickly adopted as the most recommended standard, JSON-LD allows structured data to be inserted into the site in the form of a small JavaScript script. One of the main benefits of JSON-LD is its flexibility: not only can it be inserted into the body or head of HTML pages, but it also allows structured data to be separated from the content, reducing interference with the structure of a page. It does not require editing the HTML markup directly, which makes it much easier and cleaner to manage, especially on dynamic platforms or content management systems where people prefer not to intervene directly in the page code.
Another format supported by Schema.org is RDFa (Resource Description Framework in Attributes), used mainly in more advanced contexts where it is important to describe very precise relationships between the entities represented. RDFa offers greater expressiveness than Microdata and allows the incorporation of metadata not only into HTML5, but also into other document types such as XML or SVG. Although versatile, the implementation of RDFa requires more technical complexity than Microdata or JSON-LD , and for this reason it tends to be less widely used, at least in the context of classic Web pages.
Despite the diversity of formats, the general trend in the SEO industry is clear: the use of JSON-LD is now preferred not only because of its ease of integration without direct intervention on HTML, but also because of its better compatibility with search engines, including Google, which has recommended the use of JSON-LD in several venues and official documentations. However, Microdata and RDFa continue to be widely supported and, depending on specific technical or legacy needs, may be the most suitable format in certain circumstances.
Microdata vs. JSON-LD: comparing solutions.
When it comes to implementing structured data on a website, the two main formats that often come into play are Microdata and JSON-LD. Both aim at the same goal: to provide search engines with structured information that allows them to correctly interpret page content. However, the differences between the two formats are not insignificant, and the adoption of one or the other can greatly affect technical choices and website maintenance.
Microdata, introduced in conjunction with the launch of Schema.org, requires markup to be embedded directly into the HTML code of a page. For each piece of information to be structured, attributes are added directly to existing elements. From a practical standpoint, this means nesting Schema.org vocabulary within the content that is already there, adding a series of attributes such as itemscope, itemtype, itemprop to each HTML element. Although this approach may initially be simple to implement in static sites or small projects, it brings with it problems of maintainability, especially in dynamic contexts or with complex CMSs. Any changes to the layout or design of the page force constant touching of the markup requiring attention and care, factors that reduce its efficiency in the long run.
JSON-LD, on the other hand, has more recently established itself as the preferred and most recommended solution by search engines, including Google; unlike Microdata, JSON-LD does not require intrusion into the HTML code of the page. Structured data is entered separately through a script in JSON format. This format allows the same information to be represented with a much greater degree of flexibility : a simple <script type=”application/ld+json ”> tag is all that is needed to enclose the markup, thus leaving the underlying HTML code intact. For those managing sites on dynamic platforms or frameworks where HTML code is not easily accessible, this separation is crucial. JSON-LD not only reduces the risk of errors, but also allows for quick data updates without touching the visual structure of pages.
The other aspect that should not be underestimated is compatibility, which also plays in favor of JSON-LD. Since 2015, Google and other search engines have clearly recommended the use of JSON-LD as the preferred format for implementing structured data, precisely because of its simplicity and maintainability. Although Microdata is still fully supported, it is clear that the future focus of updates and new implementations will be in the direction of the JSON-LD format .
In terms of SEO performance, there are no major differences between the two formats. Both are capable of producing rich snippets and multimedia results, but it is the practicality and scalability of JSON-LD that gives it the edge. In particular, for websites of a certain size or that require frequent updates, JSON-LD allows these changes to be handled more efficiently and with less risk of breaking HTML code.
Schema.org: how to implement markup to optimize pages
In addition to the choice of format, the proper implementation of Schema.org markup and the insertion process can also vary depending on the platform on which the site is built and our own specific needs.
For those using mature CMS platforms such as WordPress , there are dedicated plugins that greatly simplify schema integration. Popular plugins, such as Yoast SEO or Rank Math, allow you to add markup automatically, simply by configuring global site settings or by making targeted changes on specific pages, for example to manage entities such as articles, products and reviews. These tools help to properly implement the necessary structured data without requiring direct code intervention, although it remains important to verify their operation through specialized testing tools.
Sites with custom or more dynamic architectures, on the other hand, may require manual integration of JSON-LD markup In this case, the scripting language is inserted directly into the HTML of the page, typically within a <script> in the header(<head>) or body(<body>) of the page. One of the advantages of this solution, especially on complex platforms, is that it allows the separation of content display and structured data to be maintained , reducing the risk of accidental changes to the site design, and simplifying any updates.
Regardless of the approach chosen, a key post-implementation step is validation. In order for structured data to be properly recognized by search engines, that is, it is important to sift through pages with tools dedicated to checking markup correctness, such as Google’s Multimedia Results Testing Tool . This process makes it possible to detect any syntax or configuration errors, or the mismatch between the marked-up information and the content actually visible on the page.
Best practices for schema markup implementation.
Proper implementation of structured data with Schema.org , however, requires attention to specific techniques and strategic planning to maximize the effectiveness of the markup and ensure that search engines interpret it correctly. While it is not complicated to integrate Schema.org markup, following some best practices can help you avoid common mistakes and make the most of the possibilities offered by structured data.
One of the first recommendations is not to mark up everything indiscriminately: only content that has actual value to those who see it should be the subject of markup . If a piece of content is not visible to the user (for example, it is hidden via display:none or is not immediately accessible in the page view), it is essential not to include it in the structured markup. Google, as well as other search engines, may disregard such content or even penalize marked-up elements that do not contribute to the transparency and clarity of the actual content for the user.
Consistency between the marked-up content and the actual data visible on the page is equally critical. Each value embedded in the markup must match exactly what is presented on the page itself. This helps not only crawlers, but also users interfacing with rich results in SERPs, providing as transparent and consistent an experience as possible between preview and actual content.
From a technical perspective, the implementation of JSON-LD is generally preferable, as clarified above. Although Microdata and RDFa are still supported, JSON-LD offers more flexibility and ease of management, especially in more dynamic contexts where one needs to separate HTML from structured data markup. In addition, the use of JSON-LD is strongly recommended by major search engines, particularly Google, and allows future changes to be made with less impact on site structure.
Another important best practice concerns the frequency and accuracy of post-implementation checks. In addition to making sure the markup is technically correct, it is useful to constantly monitor its effectiveness. Tools such as Google Search Console ‘s Media Results Report help identify any problems with structured data and allow us to visualize errors related to interpretation or display in SERPs. In this way, we can better understand which sections of pages and which types of markup are working properly and which ones need attention.
Finally, it is advisable not to abuse Schema.org markup. Snippets of reviews, products, or other types of elements displayed in SERPs should be used sparingly and only where they are actually useful and relevant to those searching for that type of content. Otherwise, there is a risk of compromising user experience and getting penalties from search engines. Schema.org is a powerful tool, but it must be used responsibly and integrated wisely, with the specific goal of making it easier for both machines and users to understand content.
The main types of schema to use: a comprehensive guide
The power of Schema.org lies in its ability to describe a wide range of entities and concepts in a structured way, giving search engines the right signals to understand what is on a page.
Schema.org markers, referred to as “types,” are specific implementations designed to mark the various entities present in a Web site’s content, from products to events to reviews and more.
The importance of correctly choosing and implementing the right schema type cannot be underestimated, since the rich snippets and multimedia results we see in SERPs almost always depend on the correct structuring of these entities.
Let us now see what are the main schema types frequently used in SEO contexts and how to integrate them to maximize benefits in search engines.
- Schema for Products and eCommerce
If the site sells products, the most correct schema to use is Product, which allows you to describe details about each product, such as name, price, rating and availability. For example:
- name for the product name.
- offers to represent available prices and offers.
- aggregateRating for aggregate user or customer reviews.
Adopting Product markup allows rich snippets to be generated in the SERP, showing users immediate information such as rating and availability, thus increasing the likelihood of a click.
- Schema for Reviews
Reviews have been one of the main elements for generating useful rich results in SERPs for years. Using the Review type allows user reviews to be highlighted directly in the search result. Implementing this schema can be vital for e-Commerce sites, restaurants, hotels, or even individual articles and blog posts.
Key elements include:
- reviewRating to define the overall numerical rating (e.g., out of 5 stars).
- author to specify the reviewer.
- reviewBody to contain the text of the review.
These elements improve the relevance of the content perceived by users, who are captured by the presence of useful information directly on the SERP.
- Schema for Events
For those who organize or promote events on their site, falling under the Event type makes it possible to report the location, date, cost and other relevant information. Google fully supports this schema, giving the ability to show events in its SERPs with clearly visible details, especially in the local area.
Among the most important properties are:
- location , to identify the location, which can be either physical or a VirtualLocation (e.g., for live streaming events).
- startDate and endDate to indicate the duration of the event.
- offers to describe information related to purchasing tickets or access passes.
- Template for Organizations and Local Businesses.
For local businesses and stores, implementing the Organization or LocalBusiness schema helps mark up information such as address, phone number, opening hours and reviews, facilitating visibility in Google Business Profile results or local searches.
Essential properties include:
- address, which can include structured markup for PostalAddress .
- telephone, to identify contact information.
- openingHours, for opening hours dynamically linked to seasonal variations or holidays.
Using these diagrams correctly is essential for those aiming for local SEO and making sure their business shows up in maps and geo-localized searches.
- FAQs and How-tos
In recent years, FAQ and How-to diagrams are gaining in importance in SERPs, with Google showing them more and more often to answer informative queries. The FAQPage schema makes it easy to structure a page with frequently asked questions, providing direct answers to users’ problems, visible directly in the search result.
- mainEntity identifies FAQs, where each question and answer can be marked separately with Question and Answer.
Also, if the site contains step-by-step instructions, such as recipes or technical guides, using the HowTo schema allows each step to be represented in detail. This can generate specific rich snippets that help users immediately understand how to solve a problem.
- Schema for Articles and Blogs
If the site deals with editorial content, using Article or BlogPosting is the basis for markup related to articles or blog posts. In addition to leveraging the more generic CreativeWork type , Article allows you to optimize metadata related to articles, such as title and author, aiding relevance for Google News and other content indexing platforms.
Key properties include:
- headline (article title).
- author (author of the article).
- datePublished for the date of publication.
In this way we can have support in improving the visibility of articles in news or editorial relaunches.
Structured data verification tools and markup validators.
Once we have implemented structured data using Schema.org, the next step is to make sure that everything is working properly. Markup verification is essential to avoid errors that could prevent search engines from correctly interpreting our content. Currently, there are several separate tools, developed to test markup for correctness and compatibility with search engines and Schema.org.
- Google’s Multimedia Results Testing Tool.
The main tool offered by Google to validate structured data is the Multimedia Results Testing Tool. This tool is essential for testing whether pages are suitable for generating rich results such as reviews, products, events, or FAQs directly in SERPs. Unlike the old Structured Data Testing Tool, which Google discontinued in 2021, this new tool is specific to rich snippets and is part of Google’s new strategic direction toward a focus on multimedia results.
Using it is simple: just enter the page URL or paste the markup directly. Google will analyze the JSON-LD or Microdata to flag any syntax errors or problems that might prevent rich results from appearing correctly. It is the go-to tool for those seeking to gain visibility through structured data.
- Google Search Console
In addition to the testing tool, the Google Search Console plays a crucial role in long-term monitoring of structured data performance. In addition to indexing errors and page performance, Search Console provides a structured data report that tracks any problems with rich results , including:
- Critical errors or warnings that could block the display of rich snippets.
- Detailed reports on specific implementations, such as reviews or products.
- Indications of how many pages are eligible to show rich results and any problems identified by crawlers.
Search Console also offers the ability to request re-indexing of a page after making corrections, to quickly check if the problem has been resolved.
- Schema Markup Validator
After the discontinuation of Google’s Structured Data Testing Tool , the Schema Markup Validator, developed by the W3C community , has taken its place as a general validation tool for Schema.org. This tool is not focused exclusively on rich results, but tests the correctness of structured data for any type of markup implemented with Schema.org, whether using Microdata , RDFa or JSON-LD.
The Schema Markup Validator is not tied to specific Google features, but rather provides universal and comprehensive validation of Schema.org vocabulary, representing a crucial tool for more complex implementations or for sites that require more extensive validation.
- Bing Markup Validator
Bing also offers its own tool for validating structured data, called Bing Markup Validator. Although less widely used, this tool can prove useful for optimizing presence on alternative search engines, especially in markets where Bing has a sizable presence. Similar to Google, this validator analyzes any problems with structured markup, flagging syntax or implementation errors.
- Other third-party tools
In addition to the official tools, there are several third-party tools available to verify Schema.org markup , such as Yandex Microformat Validator, while tools such as Merkle Schema Markup Generator allow not only validation but also markup generation. These tools also offer detailed feedback and suggestions for improving the way structured data is integrated.
Final tips on Schema.org
Ultimately, using Schema.org’s page markups is a recommended course of action for all sites, not least because you can anticipate new evolutions in the systems by which search engines leverage such data to deliver more targeted and accurate results to queries. However, it is good to remember two things: not all types of information entered into schema.org are then actually displayed in search results, but it is easy to imagine that there are other accelerations coming.
As a general rule, then, you should mark up only content that is actually visible to people visiting the Web page, and therefore do not use schema.org for content in hidden elements or hidden divs.
Here are some essential tips to ensure that markup is implemented and optimized as effectively as possible:
- Prioritize content quality. Although the use of structured data is vital, the central role of quality content should not be forgotten . Schema.org will not magically improve a page if the content does not actually meet user needs. Making sure content is relevant, useful, and up-to-date is always the first step to SEO success .
- Know what formats are supported. While JSON-LD is generally the most recommended format for easy management and future flexibility, it is important to be aware of the advantages and difficulties of each supported format (Microdata, RDFa). In many situations, when developing more complex sites or when managing an existing site with older markup, it may be necessary to balance the best solutions for the particular case.
- Constant evolution of the.orgvocabulary continues to grow and evolve, with new schema types and properties being added to accommodate the variety of content that the Web prospects. It is important to stay abreast of new versions and understand how to take advantage of new opportunities. You should periodically monitor released versions, available either on the official website (schema.org/docs/releases.html) or through open discussion platforms such as GitHub, to find useful new features to implement on your own site.
- Integration with monitoring tools. In addition to validating your markup, it is essential to use SEO platforms such as SEOZoom to monitor the effect of implementing structured data on page performance over time. Although it does not perform markup validation itself, SEOZoom helps measure traffic and rankings, allowing you to directly correlate the impact of rich results or optimizations based on structured data.
- Do not put implementation on the back burner. Technical SEO is just as important as content or copy-pasting the right keywords. It is not strategic to postpone the adoption of structured data any longer , as Google continues to give centrality to semantic markups to provide precise and contextual answers to users. Those who do not integrate such solutions risk losing visibility opportunities to those who invest in semantic SEO optimization instead .
- Monitor results in SERPs. Once Schema.org markup has been implemented, it is essential to track results in SERPs to monitor whether rich snippets or rich results have been triggered. Use the validation tools mentioned earlier to identify possible errors or improvements and, in parallel, keep an eye on Google Search Console logs to assess concrete impacts in terms of click-through rate (CTR) and ranking.
