Structured Data Testing Tool Bye Bye! Top 6 alternatives to validate your markup

Structured Data Testing Tool Bye Bye! Top 6 alternatives to validate your markup

Google’s decision to shut down the Structured Data Testing Tool to enhance Rich Results Test usage has raised an engaging debate in the world of SEO. Is this good news for experts around the world or it’s time to look for better alternatives? We might have the answer to this question.

On July 7, 2020, Google announced the upcoming shutdown of the Structured Data Testing Tool, an instrument widely used to date by SEO experts to verify the correct implementation of the structured data on a webpage. The decision is closely linked to the announcement of the release from the beta version of a new, more effective, testing tool, Rich Results Test. As Google explains:

“Rich results are experiences on Google Search that go beyond the standard blue link. They’re powered by structured data and can include carousels, images, or other non-textual elements. Over the last couple of years, we’ve developed the Rich Results Test to help you test your structured data and preview your rich results.”

To announce the transition from one tool to another, Google has also added a new message to the closing tool.

As you can read in the official documentation, the new tool brings more advantages to the analysis and more targeted advice on improving structured data, including:

  • Showing which Search feature enhancements are valid for the markup you are providing
  • Handling dynamically loaded structured data markup more effectively
  • Rendering both mobile and desktop versions of a result
  • It is fully aligned with Search Console reports

The tool can be used to test both code snippets and web page URLs and provides users with errors and warnings. The errors prevent a page from being displayed with the multimedia results in SERP, while the warnings indicate that one or more elements concerned will not be shown in the rich results. For example, as specified in the documentation, if there was a warning for a missing image property, that page could still appear as a rich result, just without an image.

The analysis of valid structured data on Rich Results Test
Here’s how the new tool highlights errors and helps you improve them

As announced by Google earlier this month, Rich Results Test is finally out of beta and fully supports all Google Search rich result features. The tool was born in 2017 as a solution to test rich snippets, rich cards, and all other multimedia search results. When it was launched, however, it only supported four types of structured data: recipes, job listings, films, and courses. It has now been updated and finally supports all types of structured data that can be seen in SERP on Google.

Rich Results Test: is it the best solution to test structured data? What are the limits of the tool?

Rich Results Test is ready to replace the old Structured Data Testing Tool. Is it good news? For now only in part, as the international SEO consultant Aleyda Solis points out in a tweet:

When Aleyda Solis wrote this feedback it was clear that Rich Results Test wouldn’t support all types of structured data, but only those that trigger Google Rich Results. Turns out she wasn’t the only one raising the issue and this week Google’s John Mueller said that the company heard the feedbacks and that “we are planning on expanding the Rich Results Testing Tool.”

As he explained, the original idea was to simplify the job for those who were only interested in the “types of structured data that actually have an effect in search. And that’s why we focus on the Rich Results Tool which focuses on the things that we would show as Google in the search results.” But SEO experts want it all. We’ll stay updated to learn more about future improvements to the new testing tools.

However, the launching of a new tool is always an exciting time to discover new features and understand how they can help us improve our content to win the front row seats on Google Search, especially if we are talking about Rich Results. But it also opens up an important question: what if there are other, better, tools out there? Let’s take off the tooth right away and find out the best alternatives outside the Googleplex.

Top Structured Data Testing Tools

First of all: do you really need to use a structured data testing tool? Absolutely yes. These testing tools are extremely useful as they give you a lot of important information on the deployment of the structured data in your web pages, providing insights about how the search engines read these data and if they are eligible for Rich Results. Of course, each testing tool is different and can help you improve your structured data through several features. Let’s take a look at the most interesting structured data testing tool out there!

SEO Site Checkup

Price: $39.95 with a 14-day free trial

SEO SiteCheckup is a website analysis tool that contains more than one tool, including the “dear old” Structured Data Testing Tool, in one-window service. All you need to do is paste the URL of the site and click Checkup to validate the structured data, check the schema usage, monitor your website SEO, and display any issues that need to be fixed such as page load speed, URL redirects, and mobile responsiveness.

Yandex Structured Data Validator

Price: Free

If you think you’ll miss the Structured Data Testing Tool, Yandex Structured Data Validator is a suitable alternative as is very similar to the Google tool. Along with check the markup on your site, this site helps you monitor how the structured data is processed and “seen” by search engines and whether the crawlers will be able to extract the information present in the structured data. 

RDF Translator

Price: Free

RDF Translator is a multi-format conversion tool for structured markup. The main value of this tool is that, unlike most other free tools out there, it supports data formats such as XML, N3, and N-Triples. Along with the use of RDF Translator to validate your structured data, you can also incorporate the tool on your website, as it comes with REST API for developers.

JSON-LD Playground

Price: Free

It comes by itself that JSON-LD Playground is the best tool for validating JSON-LD structured data format. The use is quite simple: you just have to enter the markup code with <script type=’application/ld+json‘> or the URL of the remote document and wait to get a detailed report.

Bing Markup Validator

Price: Free

Bing Markup Validator is a part of the Bing Webmaster Tools that also includes SEO Analyzer and Keyword Research Tool. This tool is particularly useful to verify your webpages markup and get an on-demand report that helps you validate different types of structured data such as HTML Microdata, RDFa, JSON-LD, OpenGraph, and Schema.org.

Structured Data Linter

Price: Free

Structured Data Linter is a pretty minimalistic tool that helps you verify the structured data present in your web pages by simply pasting the URL of a page or a code or by just uploading a file. It supports RDFa and JSON-LD but at the moment does not support microformats.

We’ve seen the best alternative to Google’s Rich Results Testing Tool, but what about data quality monitoring?

Ok, at this point you have an overview of the new Rich Results Tool and of the most suitable alternatives out there that will help you check the markup in your web pages. But is that the best you can get? Our answer is simply: no.

As avid structured data users ourselves, having developed a powerful AI SEO tool that relies on data quality in order to enhance the content of a website and make sure that connects in the right way with search engines, we decided to build our own testing and monitoring tool.

Yes, you heard right! We think to know exactly what you need not only to validate structured data and find any error but also to do it in a smart, time-saving way. How? Take a look at the most relevant features of our tool:

  1. UPTIME. Test your structured data availability automatically worldwide
  2. VALIDATION. Ensure that data is always valid. We alert when something breaks, or if Google’s rules have changed
  3. ALERTING. Get alerted by WordLift when errors or warnings are found
  4. GUIDES. Learn how to improve your website rich result’s performance

Testing is crucial, but what about monitoring? Our new WordLift tool not only gives you an exhaustive report to constantly keep control of your quality data but also alerts you when you need to intervene, making your job easy and your markup secure.  

Uh, didn’t I tell you? You can also take advantage of our dedicated technical support!

The Role of Content Structuring in Voice Search and Beyond

The Role of Content Structuring in Voice Search and Beyond

In 1996 Bill Gates wrote “Content is King”, predicting a world where content would have been the main wealth on the Internet. Although this prediction has been a catchphrase in the contest of digital marketing in the 2000s, nowadays it could sound a little naive. It isn’t, if you rethink content separating it from its containers and try to understand and follow its law

Don’t think in terms of pages, think in terms of entities

Pages are just one of a thousand ways in which content can be rendered and displayed to your users. An entity is the real single brick of your content strategy. It can be displayed through a page, but it’s something more. It is a thing (or a person, a place, an event, etc.) that has its own properties and relationships with other things. 

Adding schema.org markup to your content you can define and describe your entities and help search engines better understand your content. Let’s say for example you have a recipe: as an entity, this recipe will have many properties such as recipeCuisine, recipeIngredient, recipeInstructions, recipeYield… and a lot more. All these properties can connect the entity to other entities or just exist as single data points. 

On the left side, you see a recipe on IINH, as users would see it on the web page, while on the right side, you have the same recipe as Google sees it through structured data.

Let’s say I’m looking for an apple pie with one single egg — because I have just one in the fridge, semantic search engines could give me the right recipe thanks to the additional information related to the entity. So, in the end, entities allow you to give a better answer to your potential readers

The same recipe presented on Google’s SERP in the recipe carousel

Why are entities relevant in this context?

Structured content can help you build Actions for the Google Assistant upon some entity types such as recipes, how-tos, news articles and podcasts. And here is how schema.org markup comes handy for voice search

Moving from building pages to creating structured data helps us create relationships between entities that matter. Entities are not isolated items, they are all connected into a cluster which is semantically meaningful.

This means that through entities you can feature different angles of a complex thing. 

For example connecting all information related to a course or a webinar across multiple pages can be strategic to stand out on Google search and is the best way to answer to different user intents. 

Structure your content building your own content model — and stick with that

As I said before, entities are just the first brick of your content strategy. Content modeling is the law that underlies your content. Structuring the content of your website allows you to reuse it in different formats and match different search intents. 

For example, the content model of the WordLift Academy allows us to repurpose our content in different formats. Each main content is a webinar which is connected to different data points such as creation date and duration, other entities such as the topics covered during the webinar and the main speaker, and media such as the cover image, the profile picture of the speaker, and the video recording. 

The Entity-based content model of the WordLift Academy

All this information can answer to different search intents and function as different entry points to the main content. 

Experiment new formats starting from your content wealth  💎

Now, let me tell you a story. 

Recently, we’ve joined Google’s Mini Apps Early Access Pilot. The idea was to offer to the user an app experience built into the SERP to navigate into the Academy content. 

I won’t enter into the details of the technological stack used to create this Mini App prototype through Google’s console. What matters here is that, having a structured content we have refined the search for WordLift courses allowing the users to navigate through them by selecting one or more topics of interest and/or a speaker. 

A preview of the WordLift Mini App and some query examples

As the pilot has been shut down due to COVID-19, you won’t see it on the SERP anytime soon. 😭 But… we are planning the same structure — well, with a few changes in terms of technology – to build an Assistant App for Google. 🚀

So users will be able, for example, to look for all the webinars about SEO by Jason Barnard on our Academy just invoking the App.  

What’s the take-away of this story? 

Formats may change and evolve, experiments come and go… but a strong content model allows you to reuse your content in different environments.

Is Voice Search Here to Stay? It is now 2020

If you want to learn more about how voice search is evolving in 2020, have a look at the webinar below, with me and Georgie Kemp getting deeper into this topic.

Is Voice Here to Stay? It is now 2020 — Streamed live on May 22, 2020 by Authoritas
Named-entity recognition

Named-entity recognition

Named-entity recognition (NER) (also known as entity identification and entity extraction) is a subtask of information extraction that seeks to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, places, expressions of times, quantities, monetary values, percentages and more.

Most research on NER systems starts with an unannotated block of text, such as this one: “WordLift is a plugin for WordPress” and extracting all relevant information from it:

What is an Entity anyway?

An entity is the “thing” described in a document. An entity helps computers understand everything you know about a person, an organization or a place mentioned in a document. All these facts are organized in statements known as triples that are expressed in the form of subject, predicate, and object. 

How WordLift is using Named Entity Recognition

Let’s get into more details as this is one of the key technologies of WordLift:

First and foremost Named-entity recognition (NER) uses a KB (Knowledge Base) that contains all known concepts (Named Entities) that needs to be extracted from a block of text.

WordLift derives semantic information from the user’s content by leveraging on freely available datasets such as DBpedia and the user’s local vocabulary.

As new concepts are added in the local vocabulary, WordLift learns the knowledge domain of the user and improve its understanding of the content.

WordLift uses a sophisticated ‘name-entity disambiguation‘ (NED) mechanism to correctly detected locations, company and people to unique “instances” in the web of data.

During the extraction phase low level NLP functions take place including POS (part of speech) tagging, tokenisation, sentence boundary detection, capitalization rules and in-document coreference.

As result of the extraction WordLift proposes to the user a set of candidate kb entities for a mention.

Learn more about Natural Language Processing

Natural language processing (or NLP) is a field of computer scienceartificial intelligence, and linguistics that has to do with the interactions between computers and humans using natural languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve natural language understanding — that is, enabling computers to derive meaning from human or natural language input.

Read more about the NLP.

Natural language processing

Natural language processing

What is natural language processing?

Natural language processing (or NLP) is a field of computer science, artificial intelligence, and linguistics that has to do with the interactions between computers and humans using natural languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve natural language understanding — that is, enabling computers to derive meaning from human or natural language input.

How does Google use NLP?

Since Google Knowledge Graph and Hummingbird were released, Google reads human language, through Natural Language Processing.

When I type in Google’s search box “moon distance,” that is what I get:

Distance of the moon from earth - Google Knowledge Graph

You may think this is simple keyword matching, but it is not. In fact, if I ask “How far is the moon?” I get the same answer.

Google’s ability to understand language goes further. If I search “moon distance in meters” that is what I get:

Distance of moon from earth in meters - Google Knowledge Graph

In short, Google knows I’m referring to the same thing and gives me the proper answer.

How does WordLift uses NLP?

WordLift suggests to content editors relevant fact-based information, images and links by analyzing content being written (either pages or post).

WordLift uses Named Entity Recognition (NER) and Named Entity Disambiguation (NED) to extract Named Entities from textual contents.

Editors can reconcile entities extracted from their posts and pages with equivalent entities available on other sources (i.e. DBpedia or Wikidata). By automatically linking entities WordLift helps machines unambiguously interpret the context of the content being written.

This information, derived from these large open graphs such as DBpedia, is also used by WordLift to add the semantic markup using the vocabulary of schema.org.

What are Word Embeddings?

Word embeddings (or text embeddings) are a type of algebraic representation of words that allows words with similar meaning to have similar mathematical representation. A vector is an array of numbers of a particular dimension. We calculate how close or distant two words are by measuring the distance between these vectors.

Read more on how to optimize your Title tag SEO using text embeddings.

How does NLP help you with SEO?

As we mentioned before in this article, NLP helps machines understand the context of what humans are writing. Combining NLP with semantic web technologies can significantly help you improve SEO and gain you a fair advantage over your competitors. If you are curious to know more about this topic, you can read our article about how advanced SEO strategies using NLP can help you improve your rankings. The article explains everything in detail and provides also some useful examples and cases.

Does structured data create a competitive advantage in SEO?

Does structured data create a competitive advantage in SEO?

Adding structured data to your website can be a wise move for your digital marketing if you want to gain organic traffic from search engines. In this article, we will discuss the reasons why nowadays you need structured data to compete on the SERP, what’s the impact of structured data and what’s its usage around the web.

The ROI of Structured Data

A short premise

Marketing is all about intercepting the right potential clients to increase sales of products or services. Under the names of different techniques and tactics, that’s it really. Digital marketing and SEO are no exceptions to this rule. In the end of the day, marketers and business owners need to know how the marketing effort is going to pay back.

Structured data and knowledge graphs are the core of our SEO services, here at WordLift, and working with a wide range of clients from different countries and industries, there are a few questions that occur quite often:

  1. What’s the impact of structured data on my site in terms of ROI?
  2. How do I measure the impact of structured data on my SEO strategy?
  3. What’s the real business value that comes with structured data?

The point, behind each of these questions is: how does structured data impact my bottom line?

Although the answer can be very specific for each business, combined with the characteristics of its website and vertical, it’s generally true that structured data nowadays can create a competitive advantage SEO, content findability, and content reuse.

What’s the impact of structured data on SEO?

Recently, Google’s Search Liaison, Danny Sullivan, clarified that structured data is optional and does not impact search rankings. Although the context of this conversation was very specific (adding the property “calories” to a recipe), the case can be considered paradigmatic of Google approach.

If fact, the official Twitter account of Google Search Liaison states that structured data is an option and that it is not required for rankings, BUT also adds that…

  1. “Using it may simply help pages that already rank well appear more attractive to potential visitors” — In fact structured data allows Google present content as rich results which highlight that content on the SERP and therefore results in more clicks.
  2. “Aside from web page listings, Google Search may have some special features where certain basic structured data is required to be eligible to appear, such as carousels.”

As John Mu said once…

So, there is actually an impact in SEO results when you use structured data as schema.org markup makes your content eligible for specific rich results and SERP features — which help your pages gain a higher CTR. Moreover, as suggested by John Mu within the tweet above, structured data helps search engines understand web content and serve them to the right users at the right time.

The advantages of structured data go far beyond SEO, and also include opportunities of content reuse, internal findabilty, and semantic analytics.

How many sites are using schema.org in 2020?

Our partner Woorank has crawled more than 20 million websites and checked how many of them are using schema.org today.

As you can guess from the graph below, the web is adapting quite slowly to structured data. Worldwide schema.org usage covers less than one third of the websites — and frontline runners, USA and France, are just slightly above the 40%.

This statistic alone can say that in this context schema.org markup can create a competitive advantage on the SERP. But there’s more.

It’s not just about quantity, it’s about quality

Excellent tool for Structured Markup

WordLift creates structured markup just as good (if not better) than manual generation. I’ve started recommending it during my consultations and clients are reporting great results.

Matt Diggity

SEO Specialist, Diggity Marketing

Many sites use schema.org to add a very basic markup. What can make a difference here is:

  • How much granular and accurate the structured data is?
  • What do you do with the data you are publishing?
  • Are you building a knowledge graph that allows you to reuse your data and content?

When you move from a quantity to a quality perspective, a whole new world opens up.

“While Google is sunsetting the support for data-vocabulary.org and we see an increasing usage of structured data in general, the focus becomes data quality. Are we prioritizing the highest converting content on our website? Is the data that we’re adding to our pages both clean and useful, from a search engine perspective? What story are we trying to tell with our data?”

Andrea Volpini, CEO at WordLift

Mixing JSON-LD and Microdata: All You Need to Know

Mixing JSON-LD and Microdata: All You Need to Know

In several cases you might need to mix structured data using different formats like microdata and json-ld; in this article we review the do’s and don’ts for these edge cases.

Can I mix microdata and json-ld?

Yes, it is totally fine to use both syntaxes side by side on the same page but Google will not be able to merge attributes for the same entity using the item ID unless you are using json-ld ONLY.

Let’s get into the details: 

  • I can have on the same page both syntaxes (microdata and json-ld); for instance I might use microdata to render WebPage and use json-ld for Organization;
  • I can also merge attributes related to the same entity when all the data is available in json-ld but …
  • I cannot combine information related to the same entity by item ID when this information is written in microdata and json-ld. While this is possible in principle, and a pure RDF application would be able to do it, Google does not support it, which means properties won’t be merged and, most importantly, this won’t satisfy the Rich Snippets‘ requirements.

This topic is particularly relevant as microdata remains today the most widely used format for structured data (see data below collected by Aaron Bradley from the 2019 Common Crawl’s sample) and there is a huge demand to improve structured data to gain additional visibility on Google’s SERP.

To confirm that we cannot mix attributes by item ID when combining microdata and json-ld we asked the help of several SEOs with in-depth knowledge on structured linked data, including Dan BrickleyJarno van Driel, Jono Alderson, Richard Wallis and Mark and Martha van Berkel.

Before engaging with the community we created two examples HTML pages:

  1. json-ld + microdata: here is the result validated with the Google Structured Data Testing Tool (where you will see the “Unspecified Type” error since GSDTT cannot merge the two syntaxes);
  2. json-ld + json-ld: here we can see that GSDTT supports the merge by type ID when data is written in json-ld

Interesting enough the first example would be properly rendered by the Structured Data Linter: a tool designed to help webmaster validate structured data markup. Here follows the information from the Twitter thread and the messages by Dan Brickley and Jarno van Driel:

READY TO AUTOMATE YOUR STRUCTURED DATA MARKUP?

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