Starting with entity extraction, you can determine if the content contains the appropriate semantic annotation and relevant entities to improve your ranking on Google.
In a few clicks, you can analyze the entities behind a search query and the entities behind a web page (whether it’s your page or a competitor’s) and develop a semantic content strategy that determines which entities can enrich your content and beat the competition.
The Entity Analysis is straightforward and supports hundreds of languages with many different alphabets (any language natively supported by WikiData).
It includes:
The list of entities with links to DBpedia or WiKidata,
The confidence level of each entity (a value from 0 to 1 that takes into account factors such as thematic relevance and contextual ambiguity),
The ranking of the entities in the SERP (this data is obtained during the analysis of the entities to which a query refers),
The number of occurrences in the text (i.e., how often the entity appears in the mentioned content).
You can select the most relevant entities, and the SEO Add-on will automatically create the JSON-LD for you!
To know which search queries you should boost, you can connect the SEO add-on to your Google Search Console and sort the analysis based on your traffic data. Here you will also find a column with the “Opportunity Score“, which suggests which search queries you should target to choose the best ones for your content.
Learn how to use the SEO Add-on for Google Sheets, watching this demo👇
What is the difference with WordLift?
“Things, not strings”
Amit Singhal, Google SVP of Engineering, 2012
This is the Semantic Web.
Entities that stand for an idea, concept, person, or place, are the real star of Semantic SEO. Google and the search engines are using more and more entities to figure out how to rank websites and web pages.
Therefore, keyword research is no longer enough. This is where our SEO add-on comes into play. With a few simple clicks, you can analyze and compare entities either by search queries or URLs or both and find the ones that are really relevant for your website to get a better search engine ranking.
Indeed, once you have identified the entities most relevant to your business, you can copy and paste the JSON-LD into your website to speak Google’s language and see your organic traffic grow.
If you are already using WordLift, you can analyze the entities and compare the entities annotated in your content with the entities that rank for the search query, as well as those of competitors, and then figure out how to optimize the content to rank better on Google.
WordLift is an AI-powered SEO tool that analyzes your content, identifies the most relevant concepts for your business, and allows you to insert semantic markup without needing any technical knowledge. These relevant concepts are the entities and are collected in a vocabulary. By relating the entities to each other, you can build the Knowledge Graph. It’s the infrastructure behind your website that allows Google and search engines to understand what you are talking about, and to see the relationships between pages and content, and their value.
This way, your website will rank better and get more organic traffic. Not only that, but users searching for products, services, or businesses like yours will find information that is more relevant and they will spend more time on your website and you can increase the conversion rate.
SEO Add-ons for Google Sheets™ and WordLift plugins are different, but they can work together to automate your SEO and save you more time, energy, and even money.
When you use the SEO Add-on together with WordLift, you can analyze entities and optimize your content so that Google and the search engines understand what you are talking about, and you get better rankings and more organic traffic to your website.
So, to sum up, what are the benefits of using the SEO Add-on for Google Sheets™?
Understand how to rank for a specific search query;
Learn how to optimize the ranking of your favorite content;
Compare a glance entity between competing URLs, search queries, or both;
Analyze the semantic spectrum you need to cover a specific topic.
The SEO Add-ons for Google Sheets™ is included in the Business+E-commerce plan. Or you can purchase it separated on StackSocial. If you buy the Add-On and want to add WordLift to your SEO tools, you’ll get a special discount. To learn more, talk to our team of experts.
How can we organize the taxonomy of a website to make it easier to find in search engines? What can we do with entities and what can we expect when we annotate content with them? This is the story of William Green, who founded Poem Analysis in 2016 with the goal of giving poetry a home online. William had noticed that there were not enough online resources about poetry and that people were still struggling to understand and appreciate it because they were not being helped to study it in depth.
The first important step was to create a well-organized website. But, as we know, a website without visibility is like an empty house. So, the biggest challenge at that time was to gain more and more visibility on Google and the search engines. So while a website must be designed for the user, the content that fills it must also be understandable to search engines. In this way, Google can index it and the website can get a good ranking that will bring it more visitors.
When William came across WordLift, he knew there was untapped potential here. He knew Schema.org and had already tried solutions that allowed him to annotate content and add structured data to the website, but he had not yet found the solution that could really make a difference. Let’s see closer this SEO case study.
WordLift brought its innovation, both in approach and technology.
“The ability to add entities on a scale like no other is something that every website owner should get excited about. Helping Google understand content better, and make the links, will only benefit the website, where schema is becoming a more dominant force for search engines, and into the future.
It was and is a pleasure to work with WordLift on cutting-edge SEO, particularly with such a quick thinking an Agile team. In doing so, we are able to test and create experiments that produce incredible results before most have even read new SEO schemas”.
William Green – Poem Analysis
The challenge
The goal of Poem Analysis was to achieve better ranking on Google and search engines and get more organic traffic to the website. The real challenge was to find a solution that would scale quickly and systematically given the large amount of content on the site.
It was also about increasing relevancy, making sure that Google was able to capture the right queries. It seems obvious at the beginning but, really, it is not when you look at each individual poem. Google might know who Allen Ginsberg is but might struggle to connect ‘Howl’ (one of Allen’s poems) to him.
The solution
For PoemAnalysis.com we used a version of WordLift specifically created for publishers in the CafeMedia/AdThrivenetwork.
The solution proposed by the WordLift team started from the idea of using the pre-existing taxonomyand tointegrate and inject structured data through “match terms”. This means that you can enrich your site by using categories and tags without having to use WordLift’s content classification panel to add markup. This way, a tag or category is treated like a recognized entity in the content classification box.
For PoemAnalysis.com we created a custom solution to achieve this goal and in this way, we used the well organized taxonomy of the websites to associate the entity correspondent to the category of the poet (e.i. The category of William Shakespeare has been associated with the Wikidata correspondent entity). Specifically, this was done here with Authors.
In a second testing phase, we decided to add the SameAs of the poem to the page of the poem (e.i. Shakespeare’s Sonnet 19 associated with the correspondent wikidata and DBPedia). The items we marked in the SameAs field are converted by WordLift into meaningful links to Wikidata and DBpedia. Subsequently, the Poem Analysis editorial team added SameAs to all entities on the site.
We are now working to add “Quiz” markup for Education Q&A, a format that helps students better find answers to educational questions. With structured data, the content is eligible to appear in the Education Q&A carousel in Google Search, Google Assistant, and Google Lens results. Stay tuned!
The results
As we saw in the previous section, the WordLift team worked on two actions that had a positive and measurable impact on Poem Analysis’ SEO strategy.
In the first case, the existing, well-organized taxonomy was used with the “Match Terms” feature to convert categories into entities, which were then annotated in the site content. To measure the impact of this first experiment, a control group was created that included a set of unannotated Poets with WordLift markup.
The semantic annotation brought +13% of clicks and +29.3% of impressions when comparing this year to last year. In this case, we did not include the control group, because after we did follow-ups for these URLs as well, the data is not statistically relevant at the moment (in the expansion of the experiment we moved most of the URLs of the control group into the variant group, so to date, only 5 URLs remain within the control group).
In the second case, the WordLift team worked on SameAs of the poem to the page of the Poem. Again, to measure impact, we created a control group containing a set of Poems not annotated. Using SameAS brought +59.3% of clicks and +82.9% of impressions.
The semantic annotation of the content also allowed another result: if you type the whole poem in Google, the first result that appears is poem analysis🤩
Example: William Shakespeare’s Sonnet 19
Conclusion
The story of William shows the impact of semantic annotation and how building a well-organized taxonomy helps semantic annotation of content have a positive impact on a website’s impressions and traffic.
In this case, we used the well-structured and indexed tags and categories as entities to annotate the website content. Generally, it is important that the entities are relevant and indexed by search engines to create the right sidewalk that moves both Google and the user.
Let us not forget that semantic content annotations add value to the user by providing them with information and insights that they would otherwise have to look for elsewhere. And, of course, it also creates a semantic path for Google, which can thus more easily assemble the concepts surrounding a piece of content and rank it by reinforcing the topical authority on that topic.
Natural Language Generation (NLG) is the use of artificial intelligence (AI) to generate written or spoken narratives from a set of data. NLG is related to human-computer and machine-human interaction, including computational linguistics, natural language processing (NLP), and natural language understanding (NLU).Sophisticated NLG software can be trained on large amounts of data, large amounts of numerical data, recognize patterns, and information in a way that is easy for humans to understand.
What is the difference between Natural Language Processing (NLP) and Natural Language Generation (NLG)?
NLP (Natural Language Processing) uses methods from various disciplines, such as computer science, artificial intelligence, linguistics, and data science, to enable computers to understand human language in both written and verbal forms. NLG (Natural Language Generation) is the process of producing a human language text response based on some data input. So, NLP accurately converts what you say into machine-readable data so that NLG can use that data to generate a response.
Originally, NLG systems used templates to generate text. Based on certain data or a query, an NLG system filled in the blank text. Over time, however, natural language generation systems have evolved to allow more dynamic, real-time text generation through the use of neural networks and transformers.
How can NLG help improve SEO?
Natural language generation has some incredible implications for your SEO strategy.
By using NLG systems, more content can be created faster. Not only does this save time, but it allows website owners, especially in e-commerce, to create content on a large scale.
However, when working with AI text generation for SEO, we need to be even smarter and more critical than usual. To create tangible and sustainable business value, we need to create workflows where humans collaborate with machines.
This means that we should not (yet) use GPT-3 or other transformer-based language models by letting them run free. We need to keep “control” over content quality and semantic accuracy to avoid unwanted biases and prejudices. Within this workflow, humans have the main task of nurturing semantically rich data in Knowledge Graphs.
The human side can be encoded in a knowledge graph. When we look at embedding the Knowledge Graph behind our blog for an entity like AI, the closest relationship in our small world leads us to SEO. Editors over the years have established this relationship between AI and SEO on our blog, and we can now use it to “control” content creation around one of these concepts.
In WordLift, we have developed a sophisticated approach to AI-generated content where we move from initial data collection and enrichment to active learning where the model is improved and the validation pipeline is also improved. The workflow consists of 4 steps as shown in the figure below.
What Google says about AI-generated content
Google’s Helpful Content Update, released in August of this year, has reignited the discussion around AI-generated content and how Google is addressing it.
Although not explicit, in the update Google confirms its aversion to AI-generated content and talks about content that is high quality and meets the needs of users, content that is written by people for people. Google wants to avoid low-quality content written for search engines and not for users.
The reasons for Google’s aversion to AI-generated content may be many, but the fact is that there are some truths to consider. The first is that AI-generated content performs well in search if it is of high quality. The second is that AI-generated content is still not easy to scale. Training the models is very tedious and there is still not complete confidence in the results obtained.
So what makes the difference? What makes high-quality AI-generated content that does not risk Google (and user) penalties? The answer is simple: data. As Kevin Indig also said: The biggest differentiator will be what inputs (data) companies can use to create content. And that’s where structured data comes in.
Data-to-Text Generation
The key is to build a large dataset that you can keep adding to and that can provide potential insights. GPT-3 will help you extract valuable insights from it.
Daniel Ericksson, CEO at Viable.
The crucial part of using NLG for AI-generated content in SEO is semantically rich data (that might also be beneficial for structured data). Language models trained on billions of sentences learn common language patterns and can generate natural-sounding sentences by predicting likely word sequences. However, when generating data in text, we want to produce language that is not only fluent, but also accurately reflects the content.
Structured data makes this possible. They are semantically enriched so that they provide the model with the information and attributes that can make content generation more accurate. In the case of GPT-3, for example, any data is converted to text to make it more usable, but without the structured data and the information it contains, GPT-3 would confuse even the best prompts. If you use structured data instead, you can achieve better results and outperform the competition, which is very large in this area, considering how many open automated content writing tools are available online.
Building a Knowledge Graph is essential for the effective use of NLG systems such as GPT-3. The Knowledge Graph is the dynamic infrastructure behind a website. It represents a network of entities – i.e., objects, events, situations, or concepts – and illustrates the relationships between them. Structured data is used to describe these entities. This way, your content is more easily understood by search engines and ranks better on Google.
With the Knowledge Graph, you then have the semantically enriched dataset you need to train models and create unique and original AI-generated content that respects brand identity and tone of voice.
AI-generated Content For SEO: A Real-World Case
How We Used NLG For E-Commerce
Here we would like to show you a real world case of how we used AI to create content that had a positive impact on SEO. In this case, we are talking about what we did for a corporation that includes some of the biggest international brands for accessories. So let us talk about AI-generated content for e-commerce.
This experiment proves that customized AI Product Descriptions can bring measurable SEO value to product detail pages (PDP). With AI-generated content, each variant of the same product gets a personalized description, whereas with standard workflow, all variants share the same description.
The experiment began a year and a half ago. In the first step of the workflow, we collected all the data we needed to better train and enrich the model. Thus:
Product feeds to describe.
Actual product descriptions or examples of product descriptions with the brand’s ToV;
Other content with the brand’s tone of voice (e.g., social feeds).
Brand kits and other materials related to the brand.
Then we selected the attributes. These must be available in the dataset provided and relevant to the product. The list of selected attributes was used to create the prompt.
To customize the model, fine-tuning is the way to go. We use the provided examples with product descriptions. This way, the content generated by the AI matches the products, brands, and tone of voice of the client. Once the prompt is ready, we can start creating the product description. The completion is renewed until we reach our goal that the product description is correct and validated in terms of number of characters (between 190 and 350). We will check the output with a native English speaker who will correct the generated text.
The validation process is an important step in automating the verification of the attributes described in the compilations. This way we can check the product description to see if all attributes are correct or missing. Once the compilation is validated, we share the output with the internal teams involved in the project, who approve and possibly integrate the descriptions before they go online.
An important step was to involve the internal editorial team from the beginning. Our goal is to improve their work, but they are the ones who have the rudder and need to be able to steer the boat in the right direction with the necessary adjustments.
After some testing:
We determined that we could have a consistent model across the group of sites, with TOV customized for each brand.
We have improved the level of validation and are becoming more flexible in handling the content rules we receive from the editorial and SEO teams.
Two key components are improving the quality of the data used to train the model and interacting with the content team.
As for validation, experiments with StableDiffusion and DALL-E 2 can help validate the overall quality of the final description.
With the first test conducted on one of the group’s websites, we achieved a 43.73% increase in clicks. By looking at the increase in the sales, the team estimated a potential double digit growth of the annual revenues.
Another test was performed on another site with a variant group compared to a control group. In this context, we saw a +6.19% increase in clicks. In this experiment, AI content was added to canonical URLs (which already contained a description).
How To Use NLG To Create SEO-Friendly Content
NLG applications use structured data and turn it into written narratives, writing like a human but at the speed of thousands of pages per second. NLG makes data universally understandable and aims to automate the writing of data-driven narratives, such as product descriptions for e-commerce.
Content Creation
Content creation is something that almost all e-commerce store and blog owners need to constantly invest in to produce fresh, high-quality content. Content can take many forms, including blog articles, product descriptions, knowledge bases, landing pages, and many other formats. Creating good content requires planning and a lot of effort.With the help of AI and natural language generation models, it is possible to produce good content while dramatically reducing the time it takes to create it.Remember that human monitoring, refinement, and validation remain critical to delivering excellent AI-generated content with current technology.
Generate E-Commerce Product Descriptions
You can use GPT-3 to generate product descriptions for e-commerce. Based on data provided to it during training, GPT-3 can predict which words are most likely to be used after an initial prompt and produce good human-like sentences and paragraphs. However, this is not an out of the box solution. To adapt GPT-3 to your specific needs and produce good product descriptions for your online store, you will need to go the fine-tuning route.
Make your e-commerce 404 pages “smart”
You can create a recommendation engine for your e-commerce that suggests products on the 404 error page. You can design the layout of the page using DALL-E 2 and create the error message using GPT-3. This way, your ecommerce 404 pages, which are among the most visited pages on your website, will help provide a relevant and optimal user experience.
Conversational AI has been part of the enterprise landscape since 2016, but it has entered the scene in pandemic times. It has supported customer service and provided content suggestions to help businesses and people through difficult times.
In this scenario, it has become increasingly necessary for enterprises to invest in developing conversational AI to cope with changing user behavior. Conversational AI is therefore among the top 5 categories in AI software spending and its evolution from simple rule-based bots to intuitive chatbots that can accurately mimic human responses by providing assistance to customers and company employees.
So the evolution of user behavior has also led to a change in how search engines work. Google search is increasingly becoming a dialog. The advancement of conversational search inevitably affects SEO. In this article, I’ll show you how you can build a Conversational AI by using SEO to provide users with an optimal experience that increases the number of visits and the time users spend on your site.
The Future Of Search
Search could be reimagined as a two-way conversation with question answering systems synthesizing and answering users’ questions much as a human expert.
Metzler, Donald, et al. “Rethinking search: making domain experts out of dilettantes.” ACM SIGIR Forum. Vol. 55. No. 1. New York, NY, USA: ACM, 2021.
Today, when you do a Google search, you get results appearing in different Google rankings and features: SERP, top stories, People Also Ask, Google Knowledge Panel, etc.
Thanks to conversational AI, search will take the form of a dialog between the user and the search engine, which is not only able to answer a query, but also to anticipate the user’s wishes.
If you own a website or are an SEO, then you need to be able to fit into this workflow. In “traditional” search, SEO was required to:
Rank at the top of Google’s search results for target queries, e.g., “GPT-3”.
Earn a knowledge panel in Google’s Knowledge Graph for your business, people, products, etc.
Rank relevant video content on the first page for target queries.
Answer specific questions to be featured in the People also ask (PAA) section.
With conversational AI, the plan of action changes, and to make this happen, it is necessary:
Win the SERP competition for target keywords.
Provide a great user experience on your website.
Make it easy for users to find the right content (UI, navigation, search functionality, chatbot, ..).
Optimize for user retention whenever the information is available on your website.
For this to be possible and for you to get into conversational search, you can use your content. By storing your data in the Knowledge Graph, you can make your content accessible and reusable for developing a conversational channel such as a chatbot.
Building a conversational AI channel on your website is crucial in taking advantage of the opportunities and benefits of interacting with your customers. This will allow you to leap ahead and gain a significant competitive advantage over your competitors.
But let’s go through the concrete steps that will help you build a successful conversational AI starting from SEO.
Conversational AI and SEO: Concrete Steps
In this scenario, SEO plays a crucial role. How can you build a successful conversational AI ? To do so, you need to follow the steps below.
Build your Knowledge Graph. In this way, you can leverage the power of entities and the relationships between them to provide Google with all the information it needs to understand your site content and to provide users with results relevant to their search queries.
Earn a Knowledge Panel. Through it, Google provides quick and reliable information about people and brands. Getting a Knowledge Panel on Google depends on the data. The more accurate and consistent they are, the more accessible Google will put the information together and return a perfect Knowledge Panel.
Use the VideoObject.Adding Schema Markup’s VideoObject feature, you can use video as a magnet to attract traffic to your content by building irresistible, clickable snippets.
Having a bot and having the KG + FAQ you can deliver a better user experience to the users on the website itself (and eliminate the requirement to go back to Google to search for another info).
A Real Use Case: From Google Search To WordLift’s AI-Powered Chatbot
WordLift is replicating what Google is doing (at the scale of a single website).
Leverages the same data published on the website
Creates a chatbot on top of transformer-based models
In terms of information retrieval, a chatbot works like a search engine. So what I do for Google will improve my chatbot (and my internal search). The same content we prepare for Google can also be used to train an artificial intelligence for conversations like a chatbot. And that’s exactly what we did to develop the chatbot for our documentation.
We trained our chatbot using Jina AI DocsQA’s q-a service and we extended its knowledge by adding to the indexing flow also FAQs (question and answer pairs) from our knowledge graph. Structured data content (such as FAQPage markup) is available using WordLift GraphQL end-point.
In this example, you can see how to take ownership of the user journey with WordLift.
Case 1: Google directly provides the answer to the question/search query. It’s the top page result.
Case 2: Google returns the passage that talks about GPT-3 in the article about generation product descriptions.
Case 3: Google displays the questions/answers that are added as FAQ schema markups to the article.
Developing a chatbot like WordLift’s that can replicate Google’s responses has many benefits:
Improve the user experience by providing answers to their questions
Avoid losing a user we worked hard for by keeping them on the site once we have an answer.
From an analytical point of view, increase the number of users visiting the website and increase the time spent on the website (if this happens, it means that we serve users better).
Takeaways
Rethinking Search. Search can be reimagined as a conversation.
Two visions. Know-it-all AI vs. relevant and accurate information.
No additional effort. Same content that is created to support Google ranking and visibility can be used to train an AI to converse like a chatbot.
Multibot orchestration. Stack of bots, master bot and bot grouping.
Personalization. Era of hyper personalization for users and for businesses.
Multimodality. Enable text, image, videos, etc. in a single query.
To know more about how Conversational AI meets SEO, watch the video👇
Looker Studio is a great, free data visualization tool from Google that lets you collect data in informative dashboards and reports that are easy to read, easy to share, and fully customizable. The data comes from various sources such as proprietary databases, Google Analytics, Google Search Console and social media platforms, and you can blend them without programming.
How To Create Semantic SEO Reports with WordLift
The Semantic Web has changed the way we approach web content. As we know, Google is changing the way it crawls web pages, focusing not just on keywords (which are still important) but on concepts and then entities.
Adding structured data to your website means that you are enriching your data with information that allows Google and the search engines to better understand what the content of your website is about. This way you can get better rankings, more organic traffic and provide users with a more relevant user’s experience.
So, with structured data, you can make a difference for your business. However, it is often very difficult to show the SEO structured data value and thus a semantic SEO strategy.
If you have WordLift, we have the solution for you! You can use the Looker Studio Connector to understand and show others the results you have achieved by working with entities on your website. Let’s go and see how it can help you implement your semantic SEO strategy.
Getting Started With WordLift for Google Looker Studio
With WordLift Looker Studio Connector, you can create Semantic SEO reports by loading data from your Knowledge Graph right into Looker Studio and blend it with Search Console or any other web analytics platform.
Just click on it and enter the WordLift key. And we have a GraphQL query ready for you, so you don’t need to do anything to get started. In case you are a power user and you know the query that you want to run, just continue. For example, if you are running an e-commerce website, maybe you want to query for product attributes or prices. Then be sure to keep checking the box “use report template for new report”, so you can get a shiny new report premade for you. Then click Connect. Here you can see the fields that come from the report. Finally, click Create Report!
At this point, you are close to creating your report, but two more steps are needed:
To go to the managed data sources and add your Search Console data source: choose your website, choose URL Impressions anche choose Web Type, and then click on Create.
Check the blends to verify that the data is merged from the Knowledge Graph and GSC.
Save and enjoy the report🤩 You can filter the data for EntityType and choose the period of time you prefer.
Benefits
If you use WordLift Looker Studio Connector for your semantic SEO strategy, you can have these benefits:
Everything in one place. You can create a single source of truth about your SEO and business performance.
Learn more about your audience. You get meaningful data about your content that helps you learn more about your customers and optimize your SEO strategy.
Useful data. You’ll be able to identify new queries and search intent to optimize your content.
Improve SEO reporting and gain new insights. Take your SEO reporting to the next level and easily gain valuable insights into your keyword rankings, traffic, and more.
Now you can focus on semantic web analytics and the advantage that you can gain in modern SEO without worrying about how to prove the benefits.
Discover more about how to create a Web Analytics Dashboard using Google Looker Studio, traffic data from Google Analytics, and WordLift, reading this article.
Learn how to create a Semantic SEO Report in 3 simple steps👇
Other Frequent Questions
Are Looker Studio connectors free?
As Google says, “You can build, deploy and distribute connectors for free. You and your users can use connectors in Data Studio for free.” In the case of WordLift’s Looker Studio connector, you can use it if you have an active subscription, because you need a key to create the reports.
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