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By Emilia Gjorgjevska

2 years ago

Create knowledge graph-native content through intelligent entity AI SERP analysis. Discover how to use the SEO Add-on for Google Sheets by WordLift.

Table of contents

  1. Smart content structuring
  2. Experimentation as the mother of innovation
  3. How entities make your content modular
  4. This is how the knowledge graph-native content works
  5. What should I do when working with different CMS systems?
  6. What are the metrics used in the SERP analysis process?
  7. How to deal with multiple similar entities or many entities in general?
  8. What if I don’t have keyword data to deal with?
  9. How is the SEO Add-on different compared to the WordLift plugin?

Start performing Semantic Keyword Research with the New SEO Add-on for Google Sheets™

Smart Content Structuring

Smart content structure is what enables quality omni channel user experience and it is the foundation of building a competitive content strategy. Having structural standards in the process of creating content is necessary because otherwise search engines like Google will not be able to grasp our content and algorithmically redistribute it afterward.

As a content writer, you surely know how demanding the content analysis process is and how important it is to match the right intents that your readers have while guiding them in the user buyer journey. If you lose them in the first phase, you are done. Everything stops there. There is no second chance given because searchers rarely return to websites that do not solve their problems and provide the right information back.

Experimentation As The Mother Of Innovation

We at WordLift go through the same problems in the content writing process as you do. We understand your frustrations, we simply encounter the same content and idea gaps and have trouble generating starting ideas for writing. That is why we often experiment with various external and internal tools that aid our writers in the content creation processes and what worked for us and our clients were entity-oriented content models where content knowledge-native structuring was the secret weapon in outperforming ours but also our clients’ competitors.

How Entities Make Your Content Modular

Entities combined with a knowledge graph approach and JSON-LD representation is what aids content findability and portability. When the data is structured, it can be easily reproduced in another content format, populated in a different database or moved from one place to another in general. According to Aaron Bradley, a well-established knowledge graph strategist world-wide, it is how “we liberate the meaning of content from its visual representation”. Content structure is the physical organization of content within systems. It is as simple as that.

This Is How The Knowledge Graph-Native Content Works

We are great fans of entities because they work better when they are connected. Search engines like them as well and we like to be the pioneers in adopting the general idea of building the web of interconnected meanings, just like Tim Berners Lee envisioned more than 20 years ago.

We are great fans of sharing on how we do stuff because growing alone is not fun and because we believe that growing as a community brings everyone forward faster. In simple terms, our novel content writing approach is based on forming content knowledge-native foundations with an end goal to bring impact to our clients’ SEO strategy:

  1. We gather keywords from Google Search Console, communication strategies or any keyword research process in general, just to form the basis for analysis;
  2. In the next step, our NLP and deep learning algorithm analyses the provided keywords and extracts TOP search queries that are associated with the queries that we provided as a basis in the first step;
  3. Once we are done with this, we analyze the extracted phrases and try to identify the most important entities based on manual observation but also entity confidence;
  4. Once the most important entities are identified, we build your knowledge graph out of them;
  5. Finally, our tool provides a JSON-LD output that can be used on your website.

As explained before, this way we engineer our content for search and produce knowledge graph-native structured content but also how we engineer our content for reuse in an omnichannel context.

You can build a Knowledge Graph from a SERP by using SEO Add-On for Google Sheets. To discover how you can do that, watch the video.

What Are The Metrics Used In The SERP Analysis Process?

It is important to organize your entities around the most pressing user needs. That is why we use three different columns that aid content writers in the user-intent differentiation process:

  1. The first one is the confidence interval metric mentioned before which is very similar to the salience score that Google uses in their natural language processing API. In simple terms, the idea is to have a metric which will show how relevant the entity is to the general topic of the article through semantic similarity;
  1. The second column which can also be called the entity id column is where we show the disambiguated entity ID description from DBPedia. This way, you, as a content writer, can expand the description to understand the filtered entity even better and decide whether is one of the most important entities to establish your knowledge graph-native structured content for; 
  1. The third column is the label column which is used to read the entity name as a quick filter in the decision-making process. 

What Should I Do When Working With Different CMS Systems?

If you have a different content management system (CMS) other than WordPress, do not worry – the JSON-LD code is available in the 5th step, so you can add it to your website through a plugin or Google Tag Manager. In any case, if you are using WordPress with WordLift, we natively support WordPress, so your entities will be imported right away.

How To Deal With Multiple Similar Entities Or Many Entities In General?

One way of solving this problem is using a clustering tool that will help you organize your entities into clusters to see which clusters dominate and which user intents are most prominent based on your keyword research process defined in step 1. We also recommend using a tool to generate long-tail keywords so that you can add a bit more variety to your keyword research process and avoid dealing with duplicate entities.

What If I Don’t Have Keyword Data To Deal With?

This is what we excel at. If you are operating in a niche which is very specific and starts growing now, so you don’t have much data as a result, you can just provide some starting ideas that describe your niche specifically and we will show you the most relevant entities for your niche based on SERP analysis.

How is The SEO Add-on Different Compared To The WordLift Plugin?

The SEO Add-ons for Google Sheets™ analyses the SERP by using natural language processing and deep learning techniques in the process of extracting the top entities which are the most relevant to your starting search queries. On the other side, the WordLift plugin does the disambiguation process in the background and performs entity linking between the entities and their respective definitions provided by DBpedia.

Excited to try this out? Let’s talk now 🙂

Start performing Semantic Keyword Research with the New SEO Add-on for Google Sheets™

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