The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?

The Reason Behind Semantic
SEO
Why does Google avoid the word “PageRank”

What’s up with Topical
Authority?
Am I against buying links?
No....

What’s Up with Topical
Authority?
Do I Buy Links?
Yes....

Authority?
Does SEO have to make sense?
If you don’t read, No....

Authority?
RankMerge
RankMerge = Actual Traffic + PageRank

Authority?
RankMerge
Why is PageRank for a blind librarian?

Authority?
How did I structure the concept?
Topical Authority was a protest, but not against link building.

Authority?
Topical Authority = Topical Coverage + Historical Data

Sample 1 – TheCoolist
From 800,000 to over 3,000,000 clicks a month.
Over 450,000 new ranking queries.
Language: English
Industry: Generic
TheCoolist.com

Fill Topical Gaps
Entertainment, Architecture, Social Skills –> Personality Types

Prioritize Topics
Create Momentum
Increase Ranking Priority for a Topic.

Design a Semantic Content
Network
Topical Map + Semantically Organized Content Network with a Knowledge Base

Nothing is Random in a Semantic Content Network
Contextual Vector + Contextual Hierarchy + Contextual Structure +
Contextual Connection + Contextual Coverage and Flow
Every heading, paragraph,
anchor text, list item, sentences
before listings, sentences after
listings,
First sentence after heading,
anchor texts, anchor text
positions, tables, table columns,
question and answer formats...
Everything is about context in
Semantics.

Sample 2 - Svalbardi
We sell a botle of water for 90 Euroes. My favourite business model.
A Shopify Store with 27 Articles with Semantics

Get Classified with Top
Authorities
Language: English
Industry: Luxury Water
Understand Macro Semantics (Topical Map + Context Distribution)

Topical Authority requires
Topical Consolidation
From B2B to D2C Transition.
Taking Investment.
Understand Micro Semantics (Semantic Content Network + Context Construction)

Create with Order.
Unite with Momentum.
An example of Topical Map and Semantic Content
Network Creation

A Diagram for a Topical Map
Only %5 is completed.

Understand Ranking
Algorithms
Always exceed the Quality Thresholds. (Initial and Re-ranking)

How to Take Authority of
another website?
One of the 23 SEO Case Study.
• Get associated with the Top Authority Websites.
• Create Site-wide Topical Entries and Context
Vectors.
• Get most of your traffic from the most important
topic.
• Outrank the top authorities to use Algorithmic
Hierarchy.
Optimize Macro and Micro semantics.

Practical Suggestions for Semantic SEO
Vastness-Depth-Momentum

‘X website outranks Y website for Z topic for P months’
Creates a Re-ranking Trigger for related topics.

Sample 3 - ABCFinance
What is Ranking State?

Sample 4 - Roxiecosmetics
From a Negative Ranking State to Positive One.

But When?
After a Broad Core Algorithm Update.

Where you get traffic determines main
topic of website.
Website A
Has 999 pages about Porn Has 1 page about Bible
Has 1 sessions for these 999 pages.
Has 1 million organic sessions for this
single page.
Is this a Porn or Bible website?

Link most Quality Web pages from
Homepage for Targeted Topics
Understand Quality Nodes.

Link most Quality Web pages from
Homepage for Targeted Topics

Macro and Micro Semantics
4 more Samples

Sample 5 - Nasdaq
A Company from NASDAQ. – I don’t want to pay half million, sorry.

Sample 5 - Nasdaq
I bought stocks since I do SEO... SEO doesn’t correlate with stock prices.

Sample 6 - Snuffstore
Language: German - Industry: E-cigaratte – Semantics are Language Agnostic.

Sample 7 - Kanbanize
Language: English - Industry: Task Management

Algorithmic Authorship and Content Engineering
Art of organizing humans with algorithmic rules of writing,
and content structuring according to LLMs.

Which sentence is more relevant?
Query: ‘What is a penguin’
Penguin is a flightless seabirds with flippers instead of wings that live almost
exclusively below the equator.
Penguin is a flightless seabirds that live almost exclusively below the equator.
Penguin is a flightless seabirds that live almost exclusively below the equator and they
have flippers instead of wings.
Sentence 1
Sentence 2
Sentence 3
Query: ‘Where does a Penguin Live?’

Prioritize Attributes and Contexts
Interrogative Term: ‘Where’ is a signal for place.
Penguins live below the equator, in the X, Y, Z geographies because their flippers and flightless
Seabirds nature provide.....
Query: ‘Where does a Penguin Live?’

Prioritize Attributes and Contexts
Learn Query Processing,
and Understanding.
What happens if query is a single word
Like, ‘Penguin’?

“Understanding” is needed.
• Google focused on
“Understanding”.
• Microsoft Bing followed the
same purpose, and created
“Satori”.

• It is not about Stuffing
Entities.
• It is about Creating a
Knowledge-base in the form
of Content.

• Calculate Attribute
Distance.
• Understand Semantic
Similarity.
• Understand Semantic
Relevance.

• Strength-based Re-ranking.
• Coverage-based Re-ranking.

Cost of Retrieval (Another concept for me)
Source: Search Quality Meeting: Spelling for Long Queries (Annotated) – March 12, 2012

What is the Cost of Understanding you?
Source: Improving Search Over the Years – 1 April
2020
Source: Webmaster Hangout, Zurich, 2021.

Cost of Ranking A Website can’t be higher
than Cost of Not Ranking the Website.

Which one is Cheaper?
Website B
1400 Content Item (Web Pages)
1200 Triples
94% Accuracy
7 Connected Topics
Clarity %96
Website A
600 Content Item (Web Pages)
900 Triples
59% Accuracy
3 Connected Topics
Clarity %78

To satisfy 9 Million Search Queries
I won’t rank 9 million different website.

Topical Authority is born from the idea
of Decreasing the Cost of Retrieval.

Optimize for Large Language Models, not
for Blind Librarians
Information Retrieval Information Extraction
• Query to Document Relevance
• Document to Query Relevance
• Document to Document Similarity
• Query to Query Similarity
• Query to Question, and Question to
Answer.
• Focus on Question and Answer
Generation for IR.

Generate Unique Questions
Information Responsiveness
Information Responsiveness is involving
the respective entity, attribute,
Value combinations with a macro and
micro context distribution across multiple
Semantically organized content network
to prove overall quality and relevance of
information.

Sample 9 – Entity Identity Creation
Use Information Responsiveness, Quality and Accuracy to
Change Google’s perception against Popular and High-level
PageRank sources.
If you can convince Google that ‘X is Y’ but not ‘Z’ with semantics,
İt means that your topical map will be knowledge base.
A website will rank only if they are similar to you.
Make your competitors imitate you.

Sample 9 – Entity Identity Creation

Understand Comparative Ranking

Prepare for Future Generative AI Search

Sample 10 – AI Search
Language: English
Industry: Alcoholic Drinks

Sample 10 - AI Search
Language: English
Industry: Alcoholic Drinks

Sample 11 - Human Search
Language: English
Industry: Promised to not share

LLMo Optimization for SEO
1. Fine-tune a LLM.
2. Create a Topical Map.
3. Create a Semantic Content Network.
4. Generate Content
5. Include Human Effort
6. Improve your Knowledge Base
7. Make your website a Speaking AI.
Human Effort with Microsemantic Optimization is must.

Use Natural Language Inference with
Chain of Reasoning

Optimize for Web Entity, not Website
Web Entity:
Website
Social Media Accounts
CEO
Teammates
Products
Sub-brands
Departments
Local Address
Environmental Policy
Scholarship
Website:
...

The SEO who understands,
not the SEO who imitates.

Semantics are Language Agnostic.
Source: Facilitating communications with automated assistants in multiple languages

Cross-lingual Embeddings for Semantic Search
A Cross Lingual Embeddings Example for the Sentence
‘Semantic search is an opportunity for Conversational Generative AI Search.’

Some Research Papers and Patents from
Google
Word Embeddings with Semantic Distance and Context Associations.

Google
David C. Taylor – Context and Knowledge Domains with Embeddings

Google
Hexagon is connected to Ultrasonic.
Ultrasonic is not a primary connection to Hexagon.

Google
«Hexagon» is an «Ultrasonic Wave» type.

Google
‘Quartz’ and ‘Cleaner’.
Macro Context: ‘Industrial Ultrasonic Cleaning Machines‘.

How to Understand Language Models and
Semantic Connections within them
‘Quartz’ and ‘Cleaner’.

Understand Search Engine
‘Cleaners’ and ‘ultrasonic’

‘Cleaner’ and ‘Ultrasonic’.

‘Quartz’ and ‘Ultrasonic’.

‘Quartz’ and ‘Ultrasonic’.
• ‘Quartz’ and ‘Ultrasonic’.
• Macro Context: ‘ Quartz Ultrasonic
Absorption and Measurement‘.
• Micro Context: Ultrasonic Cleaning for
Qaurtz Surfaces
• Zertec and its Products is signifier.

What is Large Language Model Optimization?
A Language Interpretiy Tool by Google is published. You can check ‘Word Compositionality’ and ‘Embeddings’
for certain contexts.

Large Language Model Optimization and Answer Engine Optimization are different technical expressions for
Semantic Search Engine Optimization.
Sequence Modeling (Word Compositionality Modeling)
İs backbone of Semantic SEO.
• What is the possibility of ‘Cat’ appears with predicates of
‘chase’, ‘eat’, or ‘fly’?.
Optimize Sequences of Words. (Sequence Modeling).

A manual exercise for ‘sensing search engine’

An Example of Relevance Configuration
‘Financial Advisor helps families to achieve financial independence.’ ‘Families achieve financial independence with the help of the financial
advisor.’
Macro Context: ‘Financial Advisor’ Macro Context: ‘Family Economics’
Possible Search Queries: ‘Financial Advisor + Family’
Possible Representative Question: ‘What does
Financial Advisor help families for?’
Possible Search Queries: ‘Family + Financial Independence’
Possible Representative Question: ‘How does a
family achieve financial independence?’

‘Purple Yam’ -> ‘Sweet Potatoes’

How does Google Prepare itself?

From Document Ranking to Contextual
Answer Ranking

From Document Ranking to Contextual
Answer Ranking
• Quality
• Sensibleness
• Specifity
• Interestingness
• Safety
• Groundedness

‘Black is the new white.’
Koray Tuğberk GÜBÜR

Thank you, Saigon!
Did I write Saigon correctly?

There is no difference between keyword stuffing
and entity stuffing.
Gibberish is gibberish.
What is a Blind Librarian?

Sources for Future AI Search with
Conversations
• Generation of text segment dependency analysis using neural networks
• Facilitating communications with automated assistants in multiple languages
• Processing techniques for text capture from a rendered document
• Training and/or determining responsive actions for natural language input using coder models
• Automatically determining language for speech recognition of spoken utterance received via an automated assistant
interface
• Non-deterministic task initiation with personal assistant module
• Systems and methods for biomechanically-based eye signals for interacting with real and virtual objects
• On-device projection neural networks for natural language understanding
• End of query detection
• Voice recognition system
• Annotations in software applications for invoking dialog system functions
• Enhancing functionalities of virtual assistants and dialog systems via plugin marketplace
• Determining Dialog States for Language Models
• Parameter collection and automatic dialog generation in dialog systems

Google merged with
Oingo
• Company started to focus on “Information Extraction”, not
to “Information Retrieval” and “PageRank”.
• Oingo is the inventor of Open Information Extraction.
• Open Information Extraction is to create a structured data
network from prose-type content.
• It requires to create a “Knowledge Base”.

Sergey Brin tried first
Semantic Search Engine
attempt in 1999.
• The First Semantic Search Engine Patent is filled in
2001.
• It focused on “Patterns” and “Relations” in databases.
• It didn’t work out due to high cost, low confidence.

Authors, Books, HTML Tags,
URL Patterns, and Queries
• Books and Authors (Tuples) were the first trial of
the Semantic Search Engine creation.
• But there were problems; fake authors, wrong
titles, wrong genre names.
Source: Extracting Patterns from Databases, Sergey Brin

DUAL Iterative Pattern
Extraction became a norm.
• Sergey Brin suggested using “Dual
Iterative Pattern Extraction
(DIPRE).
• Dual means a “tuple” in the form
of an “entity” and an “attribute”.
• Pattern recognition became a
fundamental step for semantic
search.

Google didn’t give up.
• They created “Phrase-posting” lists for
different contexts.
• They extracted “Co-occurrences” to
construct a Proximity Search methodology.
• They used tokenization, lemmatization,
and stemming for words.
• They have used TF-IDF, BM25, and Query
Likelihood models.
• They have invented Word2Vec, and GlovE.
• But none of these were good enough to
change the state from “Blind Librarian” to
“Understanding Search Engine”.

Is TF-IDF so good? No…
TF-IDF helps SEOs for many years to understand Document
Statistics, but it has some flaws.
• Longer Documents has “Higher Relevance” for TF-IDF.
• Document Frequency (Corpus Size) manipulates the
results.
• Term Frequency for X is 50, Document Size is 50,
Relevance 0.
• Term Frequency for X is 50, Document Size 51,
Relevance 1%.
• Term Frequency for X is 50, Document Size 5001,
Relevance is 90%+.
• Term Frequency is 500 (Because document is longer),
Document Size is 900, relevance will be increased
further.
We are still blind.
Source: Christophher D.
Manning, and Prabhakar
Raghavan

Is B25 so good? No…
A document that mentions “cat” 60 times is
not twice more as relevant as a document
that mentions “cat” 30 times.
• BM25 has an extra parameter (k1) to
normalize “term saturation”.
How about short articles?
• BM25 finds short articles more relevant.
• Long-form articles lost relevance.
We are still blind.
Source: Christophher D.
Manning, and Prabhakar
Raghavan
Term Saturation x Term Stuffing

An example of Lexical
Search
If query has both “USA” and “Moon”, the phrase posting list
Moon -> 24, 66, 54, 21, 09, 43, 421
USA -> 42, 31, 56, 72, 31, 54, 51
• 54 exists on both inverted index.
• “…. Bla bla bla bla….. Moon…… bla bla bla USA….. Bla bla bla…..
• We are still blind.

Lastly, Query Likelihood …
What is the possibility of ‘Query terms’ to appearing in a
document?
Query Likelihood is a transition between ‘Lexical Search’
and ‘Semantic Search’.
But still, it doesn’t understand.
Transition starts with LLMs.

“The destiny of [Google’s
search engine] is to become
that Star Trek computer, and
that’s what we are building.”
- Amit Singhal

“Google is
designed for
users, not for
websites.”
- Lawrance Page

‘Breville Juicer’ -> ‘Product’ + ‘Book’ Search

Google

The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?

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