Semantic SEO became a major part of ranking web pages when Google moved to entity search from phrase based matching to determine relevance. This means that relevance is now determined by the frequency and location of entities in the query corpus. A query corpus is all the results returned for a search. After the move to entity based search there seems to be some misunderstanding in the community about how relevance is determined.
How to Raise Semantic Relevance
To raise relevance you include the entities and the entity relationships found in the query corpus in your page using the same frequency and locations. Phrase based matching counted the matches and the context of the match. Some entity frequency is likely not as much about count as just being included, however, for the main entity frequency likely is more like phrase based matching in that the number of times it appears will raise document relevance.
Entity location is not just about where the entity appears in the web page it is also about its location in the context of other entities and its entity relationships. It’s important that the entity’s relationship to other entities and its own relationships are easily determined. This is done by placing the entity within the context of the relationships and other entities.
Why Semantic SEO Deserves a Separate Audit
The reasons for the many different types of SEO audits are:
- recommendations are implemented by a specific department or professional
- for organization of larger audits
- to analyze specific types of ranking problems
When I decided to look at what should be included in a Semantic SEO audit or if there were any benefits to adding a Semantic SEO audit I realized that presently a lot of the analysis for Semantic SEO is covered in the other types of audits including:
- Technical SEO Audit
- On-page SEO Audit
- Content Audit
- Link Audit
- Social Audit
- Local SEO Audit
Most of the above are easy to identify the Semantic SEO elements within them with the exceptions of Social and Local audits. Consider that schema is a huge part of optimizing Semantic SEO elements which Social profiles should be included in Organization or Local Business schema as SameAs items and the info in the GBP (Google Business Profile) is usually the reference for a business knowledge Panel.
Since, as mentioned earlier, Semantic SEO is how you optimize for entity search and that is a key to relevance and ranking it therefore deserves a separate audit. Also much of the optimization is implemented by more technical personnel who can write schema and do other analysis.
What Should be Included in a Semantic SEO Audit
Once I determined that there were benefits to doing a separate Semantic SEO audit I started thinking about what should be included in the audit and came up with the following:
- SERP features analysis
- Schema review
- Content & On-Page SEO analysis
I would recommend the people doing the related Semantic SEO audit do data collection and analysis at the same time as the other audits are being done but that the semantic elements within them be reported in the Semantic SEO portion of the audit.
SERP Features Analysis for a Semantic SEO Audit
I decided that SERP features should be included in this audit because rich results often require schema and for audit organization purposes there is a benefit to having all features clustered together. Audits that I have written or reviewed by others have these important SERP features in separate audit or areas. I think there is extra value in the audit if SERP features are clustered together.
The following are the SERP features that should be analyzed:
- Knowledge Panel
- Rich Results
- Featured Snippets
Knowledge Panel Analysis
Since this post is about auditing Semantic SEO implementation I will point you to this post on how to get Knowledge Panels for notable people, artists and businesses for information on how to implement any recommendations.
The first thing that should be done for knowledge panels is to determine if the knowledge panel needs to be claimed. All knowledge Panels that aren’t for a business should be claimed so you have the ability to edit the knowledge panel. Business knowledge panels are edited using your GBP (Google Business Profile).
Verify that all information in the Knowledge Panel is correct and that the Knowledge Panel appears when the business name is searched. It is important to determine the search(s) that panels appear for.
For instance I discovered that a well known architect in the UK did not get a knowledge panel because his firm name was his name so the business GBP appeared. I am sure that since he has a Wikipedia entry he could get 2 knowledge Panels with a small change to the business name in the GBP.
Rich Results Analysis
Rich Results analysis consists of reviewing the schema markup for errors and omissions which can be found in Google Search Console (GSC). Reviewing the GSC for errors and omissions is important because Google’s requirements for implementation of schema is sometimes different than schema.org.
Google Rich results test is the best way to check if schema implementation is correct and the if the page is ok for adding rich results. Not all pages are able to support rich results. For instance I discovered a page could not support rich results because a YouTube embed used robots.txt to block access to it.
Once the schema has been verified as error free the next step would be to determine if there are missed opportunities to add schema to older content or add content that includes rich snippets. Review the gallery of all Google structured data types for information about the different types of Google supported structured data and how to implement them.
Featured Snippets Analysis
The first task is to determine if there are currently any featured snippets which can usually be found in most popular rank reporting programs. Any first page rankings are candidates for featured snippets. See Featured Snippets 101: The Basics & SEO Strategies to Maximize Traffic for more information.
Schema Markup Review
Most of the information above on rich results and featured snippets verification and finding opportunities is applicable to the schema markup review. There are Google unsupported schema like services that an SEO may want to include for future proofing and other crawlers and search engines. For this markup you can use the Schema.org validator for validation.
Remember that schema is used to make the content in it machine readable. There are also item types in Google supported structured data that are not required but do make a page easier for Google to understand what the page is about.
I make sure that all blog posts have article schema and that the schema includes both About and Mentions item types. By their names you can see the value including these item types. They identify the entities the article is about and associated entities that are mentioned. This site isn’t currently adding article schema because I am developing a plugin.
Content & On-page Analysis for Semantic SEO
Although it is well understood in the industry that entities are important for raising relevance it isn’t fully understood that the entities and their relationships must be found in the query corpus in order to raise relevance.
The on-page portion for a Semantic SEO audit should do the same analysis as was done previously, however, instead of evaluating the use and context of keywords you would evaluate the use of entities and relationships.
The biggest difference is that you aren’t evaluating the use of keywords you are also evaluating the use of all entities and relationships in the query corpus. The easiest way to do this is to search Google using the query to get a list of the top results. I generally will use only 2 or 3. I like to take the first, fifth and tenth positions to see how they differ.
There are a number of entity extraction apps on the web, however, I use this free Google cloud app that shows the location and frequency of entities in a page using NLP (Natural Language Processing). By comparing your page to the results from the cloud app you can determine if location and frequency of entities and relationships is optimal.
There are three reports provided by the NLP cloud app. In addition to the entity info it scores the sentiment on sentences and paragraphs which may be important for determining intent. The third is a category classification and score which there was definite correlation between the category score and ranking of the page.
Semantic SEO Audit Conclusions
I wrote this to start a discussion about Semantic SEO in the organization of and presentation of audits. In this post I outlined why I felt there should be a separate Semantic SEO audit or section in a more detailed site audit. I discussed the elements of Semantic SEO that should be included in it from other types of SEO audits.