What are the differences between SEO and Semantic SEO?
SEO has always been marketing in the framework of the Web. It introduces site owners to consumers to allow communication between the two about goods and services offered which can be purchased by consumers to allow them to become customers and learn more about how they can take advantage of those offers.
In SEO, Pages are usually ranked in search results based upon a combination of information retrieval scores based on relevance and authority signals using things such as backlinks from other sites.
Semantic SEO is different from SEO. It focuses on real-world objects, or entities made up of things such as people, places, and things (such as ideas and concepts.) A page about entities contains information about the different elements of those entities, such as facts about the different attributes used to describe those entities, and identifiers that can help someone learning about an entity to know it and understand it better.
A Semantic SEO page can include knowledge panels, search carousels filled with entities, featured snippets that may answer questions about the entities that may appear in a query, related questions (“People also ask” questions that may be like the featured snippet answers), related entities, and more.
Entities and a Semantic Search Engine
Focusing on Entities on a Web page can make a lot of sense. For example, a website about the City of Baltimore might contain information about the people who lived there and monuments left behind in their memories. It can tell you about the famous churches and schools in the City, and well-known buildings and places and businesses.
If you were going to write about Baltimore, you would ideally dig into the history of the city, paying attention to who visitors might be most interested in finding out more about. Learn about the history that brought America its national anthem. Instead of Optimizing a page for terms or phrases on the page, write about the actual people and places and things there that tell visitors the information that they might be most interested in. Let them know about the facts associated with those entities, and the attributes for those entities.
In the mid-2000s Google had engineers working on a project they referred to as the Annotation Framework. This project was run by Andrew Hogue, who was also responsible for managing the acquisition of MetaWeb, with the volunteer directory Freebase.
You can learn more about those efforts by reading the Resume of Andrew Hogue, which includes information about what he was doing at Google during his career. He was also involved in creating a Google Tech Talk video about what Google was doing at the time that is worth watching:
More Information in Search Results Using Semantic SEO
Another aspect of Semantic SEO at Google is a move away from 10 blue links in search engines to search results that are filled with rich results. This was originally described in a Google Blog post by Ramanathan Guha and has been expanded in the post Introducing Rich Snippets by Kavi Goel, Ramanathan V. Guha, and Othar Hansson.
In 2012 Google expanded the information found in Freebase and gave us search results that provided more information about entities that appear in queries, or at least the entities that Google knows about and may have included in the knowledge graph. (See: How Google’s Knowledge Graph works)
We can see more information about entities in knowledge panels, often following knowledge templates based on the type of entity that those are about. For local business entities, we will often see sentiment-based reviews about those businesses. We can also be given query revisions related to the entities displayed in the panels. Knowledge panels often tell us about query log information about other entities that “people also search for.”
Providing More Information about Entities by Site Owners Using Schema
In 2011, Google joined with several other search engines to provide a way of providing machine-readable only information about the entities that appear on pages on the Schema.org website This approach to sharing between search engines echoes what we have seen in the development of XML sitemaps earlier.
Schema is one of the most rapidly growing areas of Semantic SEO, and more effort is undertaken to update Schema with new Releases. It is possible to be involved in discussions about new and developing schema by subscribing to the email@example.com Mail Archives
It’s not unusual for SEOs to be learning about Schema these days as part of Semantic SEO, and study new aspects of Schema as it is developed and comes out. Stars in search results for products can lead to increased clicks in SERPs and are worth learning about. I keep an eye on new emails from the schema mailing group and new revisions to schema as they come out.
Knowledge Growing On the Web in Semantic SEO
An early patent from Google was the provisional one filed by Sergey Brin in 1999 after Lawrence Page filed the PageRank patent a year earlier. I wrote about that patent in the post Google’s First Semantic Search Invention was Patented in 1999. This algorithm was named the DIPRE algorithm after “Dual Iterative Pattern Relation Expansion.” It describes a way of finding sites that contain information about specific books, and attributes of those books, such as when they were published, who the publishers were, how many pages each had, and more. If a site had all of the books, the algorithm told it to collect information about the other books that it contained at well.
At the start of last year (2020) Google filed a continuation patent that started about collecting information about books to display in search results. This continuation patent was about all kinds of entities, and not just books anymore. My post about this patent is Rich Results Patent from Google Moves on from Only Books. It provides more details about collecting facts about entities than the 2009 Google blog post about rich results did.
Google patents have also filled in a lot of details about how Google might start collecting information about entities directly from web pages. One of the most detailed about using natural language processing to collect part of speech information and entity recognition to build triples (Subject/Verb/Object) about those entities. For more details on how Google may do that see: Entity Extractions for Knowledge Graphs at Google
Expanding Meaning by Rewriting Queries
SEOs have been doing Semantic SEO for almost as long as there has been SEO. We don’t just optimize pages for keywords. We have been optimizing pages for meanings because the search engines can sometimes understand that we might be creating pages that are related to words in a query that a searcher might perform.
Back in 2003 Google started rewriting queries that people performed by replacing synonyms for words.
We saw Google develop more sophisticated ways of substituting synonyms using the Hummingbird approach, which I wrote about on the day it came out in the post: The Google Hummingbird Update and the Likely Patent Behind Hummingbird. That patent came out a few weeks before Google announced it on Google’s 15th Birthday.
Within a few years, Google was telling us about their use of an artificial language approach which used a Word Vectors Approach to rewrite ambiguous queries and expand them with potentially missing words in those queries. These queries could capture missing meanings, and answers to queries that Google was having difficulties with before. I linked to citations behind the Word Vectors Approach in the post Citations behind the Google Brain Word Vectors Approach.
We have seen Google expand the use of Natural Language Processing by using pre-training language models such as BERT in several papers from Google in the past few years. I wrote about how a Word Vector approach (like in Rankbrain) was being used for question answering, in the post Question Answering Using Text Spans With Word Vectors
Here is an example of a search result where Hummingbird is replacing the word “place” with the word “restaurant:”
This is an example that is about real-world entities, and understanding the meaning behind the words in a query It is about how SEO is becoming Semantic SEO.
SERPs Augmented with Knowledge Results
A few years ago Google Paul Haahr presented at SMX West in How Google Works: A Google Ranking Engineer’s Story:
Entities in Semantic SEO
A Google patent that came out after that presentation told us about how Google would look in a query for an entity, as Paul Haahr told us. And, if Google finds an entity in that query, it may decide to augment the search results with knowledge-based results. Again, a Semantic SEO approach to search. I wrote about this patent in Augmented Search Queries Using Knowledge Graph Information.
So again, Google is showing us that Semantic SEO focuses on finding real-world objects in queries that searchers perform and knowledge results can include featured snippets, which answer questions that many people ask about those entities. Those results can also include other questions, often referred to as “related questions,” or “people also ask” questions. The places where they may find those related questions is to crowdsource them by looking in query logs for related questions in a question graph. I wrote about those in Google Related Questions now use a Question Graph.
I pointed out the labels that Google uses for categories to my co-workers because they tell us in a Google blog post that they are associating machine ID numbers with entities in image search.
The Google post at Improving Photo Search: A Step Across the Semantic Gap
And the categories in image search show related entities and concepts in an ontology-based on your search terms, as I detailed in Google Image Search Labels Becoming More Semantic? If you are doing keyword research for pages and might want to better understand related entities and concepts for those pages, search for those terms on Google Image search, and it can tell you about entities and terms that might be related to people and places and things.
Semantic Topic Models in Semantic SEO
Back in 2006, I wrote about Anna Lynn Patterson’s Phrase-Based Indexing in the post Move over PageRank: Google is Using Phrase-Based Searching?. I expanded on this approach many times over the years. Google has been granted many related patents on different aspects of phrase-based indexing. I added to it a post called Thematic Modeling Using Related Words in Documents and Anchor Text which shows off how frequently reappearing co-occurring phrases tend to be very predictive about what the pages those are used on are about.
And a couple of years later, I wrote about a continuation patent change to Phrase-based indexing that turned it from a reranking approach to a direct ranking approach: Google Phrase-Based Indexing Updated.
Answering Questions using Knowledge Graphs
In the post Answering Questions Using Knowledge Graphs I wrote about association scores which give different weights to touples about entities, and how those use their sources to give them weight. I also write about how Google might take a query, run it, and collect the top pages as results and create a knowledge graph based on those results to provide an answer. The patent that covers this is the patent application Natural Language Processing With An N-Gram Machine.
I provide several examples of the use of search carousels showing entities that answer queries like in that patent application in the post Ranked Entities in Search Results at Google.
An Example of such a search carousel with ranked entities appears in SERPs for a query at Google such as “Best Science Fiction Books 2020.”
These books are taken from Query Results filled with entities that are shown in a carousel in a ranked order.
I have also written about how Knowledge graphs can be created as personalized results for people and used to answer questions for them in the post User-Specific Knowledge Graphs to Support Queries and Predictions
Search will collect real-world information about you and use it to answer questions that are relevant to you. That is the promise of Semantic SEO as we move into a world that includes smart devices during the internet of things with smarter cars and kitchen devices, and email connections with many others in the world.
The Web Becoming Ready for Semantic SEO?
Back in 2001, Tim Berners-Lee, James Hendler and Ora Lassila wrote about the Semantic Web for Scientific American. The sharing of information and the collection of data described in that paper tells us about the future of Semantic SEO that many places such as Google are working towards.
A more semantic Web isn’t one where pages are stuffed with synonyms or stuffed with semantically relevant words, but one as Tim Berners-Lee wrote:
The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better-enabling computers and people to work in cooperation.
I thought it was worth sharing some of the things I have been seeing from the patent office and on the pages of Google about search becoming more semantic. Most pages that are ranking in Google for terms such as “Semantic SEO” are shallow and filled with synonyms and a lack of understanding about how semantic technology might work, with mentions of technology from the 1980s and a failure to mention things such as knowledge graphs or schema.
Most of those articles don’t even contain information about either knowledge graphs or schema, and that is a problem that those don’t cover those topics.