In the context of the Internet, SEO has always been marketing.
SEO pages ranked highly in search results based on information retrieval scores derived
from backlinks from other pages and sites, as well as relevance and authority signals.
Semantic SEO connects site owners with customers to make it easier to communicate about the products and services they offer.
Semantic SEO also focuses on indexing real-world things like entities and facts,
as well as the properties that go with them.
As a result, consumers will have a simpler time finding information, becoming customers, and learning more about the offerings.
Semantic SEO is a type of technical SEO that focuses on determining query intent and responding to
any inquiries users may have about that query.
Semantic SEO entails optimizing material for entities and subjects as a whole.
A page about entities provides information on those entities’ properties,
such as facts that describe them and IDs that can be used to learn more about them.
In Semantic SEO SERPs pages, like in universal search SERPs pages in SEO,
there is a lot of variabilities.
At Google, I discovered a copy of what appears to be the second patent application.
Sergy Brin submitted a preliminary patent in 1999 for another method he called
“Dual Iterative Pattern Relation Expansion,” similar to Lawrence Page’s Provisional PageRank invention.
In my last piece, I discussed how Google’s First Semantic Search Invention was Patented in 1999.
Google submitted this provisional patent less than a year after
the PageRank Algorithm was released.
It led to Google launching the Framework Annotation Project,
which resulted in numerous patents being filed, including one for a Browseable Fact Repository,
which was administered in-house until Google acquired MetaWeb
and its volunteer-run knowledge store, Freebase.
It’s easy to see why focusing on Entities is a good idea.
A website on the city of Baltimore, for example, would include information
about the individuals who lived there, as well as monuments erected in their memory.
It can also include information on the city’s well-known churches and schools, as well as well-known buildings, sites, and businesses.
In 2005, I began conducting entity optimization, and in the essay Entity Optimization:
How I Came to Love Entities, I detailed my efforts.
In a post titled Clustering Entities in Google SERPs Updated, a more current post claims
that Google focuses more on entities in search results.
If you’re writing about Baltimore, you’ll want to learn about its past.
You should pay special attention to persons and places about which tourists might want to learn more.
Learn about the events that led to the creation of America’s national anthem.
Rather than optimizing a page for keywords or phrases, write about real people, places,
and things to provide readers with the information they’re looking for.
Inform them of the facts surrounding those entities.
Google developers were working on a project called the Annotation Framework in the mid-2000s.
Andrew Hogue, who was also in charge of the acquisition of MetaWeb, collaborated on
this initiative with the volunteer directory Freebase.
If Google is unable to develop the technology that underpins entities, they may be able to acquire it.
You may read Andrew Hogue’s resume to discover more about his achievements.
It contains details about his work at Google throughout his time there.
He also made a Google Tech Talk video describing what Google was working on at the time,
The Structured Search Engine, which is worth viewing.
Semantic SEO Provides More Facts in Search Results
Moving away from 10 blue links to search results filled with rich results is another component of Google’s
Semantic SEO strategy, as detailed in a Google Blog post by Ramanathan Guha.
Also covered in Kavi Goel, Ramanathan V. Guha, and Othar Hansson’s piece Introducing Rich Snippets.
In 2012, Google extended the data in Freebase and supplied us with search results
that included additional information about entities that appeared in queries.
or, at the very least, entities that Google is aware of and may have included into the knowledge graph.
In knowledge panels,
we can see more information about entities,
which frequently follow knowledge templates based on the sort of entity they cover.
For local business entities,
we will frequently see sentiment-based reviews,
as well as query modifications relating to the entities listed in the panels.
Query log information on other items that
“people also search for” is frequently provided by knowledge panels.
Site owners can use Schema to provide more information about entities.
In 2011, Google teamed up with other search engines to give
machine-readable data on entities that exist on Schema.org pages.
This method of sharing amongst search engines was similar to how XML sitemaps were developed previously.
Schema is a rapidly growing field of Semantic SEO, and
more effort is being put into keeping Schema up to speed with new Releases.
As part of Semantic SEO, SEOs
may now learn about Schema and research developing features of Schema.
Showing stars in product search results,
for example, can increase clicks and is something worth learning more about.
I also keep track of new emails from the schema mailing list and new Schema revisions when they become available.
After Lawrence Page submitted the PageRank patent a year prior, Sergey Brin filed an early Google patent in 1999.
I wrote about Patents in 1999 after Google’s First Semantic Search Invention.
The DIPRE algorithm, which stands for
“Dual Iterative Pattern Relation Expansion,” was the subject of this invention.
You can use it to identify websites that provide details about certain books and their characteristics,
such as when they were published,
who published them,
how many pages they had, and so on.
The algorithm advised a site to acquire information about the other books it carried
if it possessed all of the books from the patent’s seed set.
Google filed a continuation patent to collect information about books to display in search results
at the start of last year (2020).
This continuing patent covers a wide range of items, not just books.
Rich Results Patent from Google Moves on from Only Books is my post about this patent,
and it goes into greater detail about gathering facts about entities
than the 2009 Google blog post about rich results.
Google patents also reveal how the company might obtain data about entities directly from the internet.
One of the most in-depth studies on how to use natural language processing
to collect speech data and entity recognition to create triples (Subject/Verb/Object) about such entities.
See Google’s Entity Extractions for Knowledge Graphs for more information
on how they might achieve it.
Aside from the entity extraction method stated in
that patent, data wrappers used in another extraction method.
As long as Google Maps has been,
it has been using the name, address, and phone information for local search.
This method is utilized on websites that collect information about various entities.
Television episodes and songs by musical artists,
as well as company addresses, are examples.
This is yet another method in which Google uses Semantic SEO
to improve its understanding of facts that it may display as responses to searcher queries.
Rewriting Queries to Expand Meanings for Answers
Semantic SEO has practiced by SEOs for nearly as long as SEO has existed.
We don’t merely use keywords to improve pages.
Because search engines recognize that we produce sites related to
the definitions in a query that a searcher might do, we optimize pages for meanings.
Google uses the Hummingbird technique to develop more sophisticated ways of substituting synonyms,
which I reported about when it first came out in the piece
The Google Hummingbird Update and the Likely Patent Behind Hummingbird.
That patent was published a few weeks before Google’s 15th birthday when it announced.
Knowing Google’s tools and methodologies for extracting entity information from
the Web is essential to understanding Semantic SEO.
It also entails utilizing that technology to create knowledge graphs.
Wavii, which used an open domain information extraction process
that they documented in videos and publications, bought by Google in 2013.
Did Google Acquire the Future of Web Search?
Did I write about it in Did Google Acquire the Future of Web Search?
Since then, we’ve seen Google scouring the Internet for anything it can learn.
Word Vectors and natural language processing language models like BERT and MUM employed in this project.
So,It seemed worthwhile to share some of the stuff I’ve seen from the patent office,
as well as from Google’s pages regarding search becoming more semantic.
The majority of Google results for phrases like “Semantic SEO”
littered with synonyms and a lack of grasp of how semantic technology works.
So,They also discuss 1980s technology, but they don’t include knowledge graphs or Schema.