This article delves into the concepts of Semantic SEO, Topical Authority, and PageRank, exploring their relationships and how they benefit both website owners and search engines. By leveraging Natural Language Processing (NLP) techniques, Semantic SEO improves search engine comprehension of content and enhances user experience, ultimately leading to better search results.
In the ever-evolving world of Search Engine Optimization (SEO), understanding the intricate connections between Semantic SEO, Topical Authority, and PageRank is crucial for webmasters, content creators, and marketers. These concepts play a vital role in enhancing the visibility and relevance of websites in search results.
Semantic SEO: Going Beyond Keywords
Semantic SEO involves optimizing content by focusing on the meaning and context of words, phrases, and sentences rather than merely targeting specific keywords. This is achieved through NLP techniques such as topic modeling, sentiment analysis, and entity recognition, which allow search engines to comprehend the true essence of content.
Topical Authority: Establishing Expertise and Trustworthiness
Topical Authority refers to the perceived expertise of a website or content creator in a specific subject area. By producing high-quality, relevant, and in-depth content, websites can establish themselves as authorities, earning the trust of both users and search engines. This translates into higher search rankings and increased visibility.
PageRank: Measuring the Importance of Webpages
PageRank is an algorithm used by Google to determine the significance of a webpage by analyzing the quality and quantity of its inbound links. A higher PageRank implies that a website is more authoritative and valuable, thus warranting a better position in search results.
The Interrelation of Semantic SEO, Topical Authority, and PageRank
Semantic SEO, Topical Authority, and PageRank are interconnected concepts that work in tandem to improve a website's search performance. By focusing on Semantic SEO, content creators can enhance their Topical Authority and establish a solid online presence. This, in turn, can lead to higher PageRank and improved search visibility.
The Benefits of Semantic SEO for Search Engines
Semantic SEO not only benefits website owners but also search engines by reducing the cost of understanding documents. With the help of NLP techniques, search engines can efficiently analyze and comprehend content, making it easier to identify and index relevant webpages. This ultimately leads to more accurate search results and a better user experience.
In conclusion, embracing Semantic SEO, Topical Authority, and PageRank is essential for achieving higher search rankings and increased online visibility. By leveraging NLP techniques, Semantic SEO offers a more sophisticated and efficient approach to understanding and optimizing content, ultimately benefiting both website owners and search engines.
10. 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
13. Design a Semantic Content
Network
Topical Map + Semantically Organized Content Network with a Knowledge Base
14. 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.
15. Sample 2 - Svalbardi
We sell a botle of water for 90 Euroes. My favourite business model.
A Shopify Store with 27 Articles with Semantics
16. Get Classified with Top
Authorities
Language: English
Industry: Luxury Water
Understand Macro Semantics (Topical Map + Context Distribution)
21. 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.
29. 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?
30. Link most Quality Web pages from
Homepage for Targeted Topics
Understand Quality Nodes.
37. Algorithmic Authorship and Content Engineering
Art of organizing humans with algorithmic rules of writing,
and content structuring according to LLMs.
38. 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?’
39. 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?’
40. Prioritize Attributes and Contexts
Learn Query Processing,
and Understanding.
What happens if query is a single word
Like, ‘Penguin’?
41. “Understanding” is needed.
• Google focused on
“Understanding”.
• Microsoft Bing followed the
same purpose, and created
“Satori”.
42. “Understanding” is needed.
• It is not about Stuffing
Entities.
• It is about Creating a
Knowledge-base in the form
of Content.
54. 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.
55. 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.
56. 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.
67. 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.
72. Optimize for Web Entity, not Website
Web Entity:
Website
Social Media Accounts
CEO
Teammates
Products
Sub-brands
Departments
Local Address
Environmental Policy
Scholarship
Website:
...
74. The SEO who understands,
not the SEO who imitates.
75. Semantics are Language Agnostic.
Source: Facilitating communications with automated assistants in multiple languages
76. Cross-lingual Embeddings for Semantic Search
A Cross Lingual Embeddings Example for the Sentence
‘Semantic search is an opportunity for Conversational Generative AI Search.’
77. Some Research Papers and Patents from
Google
Word Embeddings with Semantic Distance and Context Associations.
78. Some Research Papers and Patents from
Google
Word Embeddings with Semantic Distance and Context Associations.
79. Some Research Papers and Patents from
Google
Word Embeddings with Semantic Distance and Context Associations.
80. Some Research Papers and Patents from
Google
David C. Taylor – Context and Knowledge Domains with Embeddings
81. Some Research Papers and Patents from
Google
Hexagon is connected to Ultrasonic.
Ultrasonic is not a primary connection to Hexagon.
82. Some Research Papers and Patents from
Google
«Hexagon» is an «Ultrasonic Wave» type.
83. Some Research Papers and Patents from
Google
‘Quartz’ and ‘Cleaner’.
Macro Context: ‘Industrial Ultrasonic Cleaning Machines‘.
84. How to Understand Language Models and
Semantic Connections within them
‘Quartz’ and ‘Cleaner’.
86. How to Understand Language Models and
Semantic Connections within them
‘Cleaner’ and ‘Ultrasonic’.
87. How to Understand Language Models and
Semantic Connections within them
‘Quartz’ and ‘Ultrasonic’.
88. How to Understand Language Models and
Semantic Connections within them
‘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.
89. What is Large Language Model Optimization?
A Language Interpretiy Tool by Google is published. You can check ‘Word Compositionality’ and ‘Embeddings’
for certain contexts.
90. How to Understand Language Models and
Semantic Connections within them
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).
91. How to Understand Language Models and
Semantic Connections within them
A manual exercise for ‘sensing search engine’
92. 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?’
93. How to Understand Language Models and
Semantic Connections within them
‘Purple Yam’ -> ‘Sweet Potatoes’
105. There is no difference between keyword stuffing
and entity stuffing.
Gibberish is gibberish.
What is a Blind Librarian?
106. 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
107. 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”.
108. 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.
109. 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
110. 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.
111. 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”.
112. 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
113. 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
114. 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.
115. 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.
116. “Understanding” is needed.
• Google focused on
“Understanding”.
• Microsoft Bing followed the
same purpose, and created
“Satori”.
117. “Understanding” is needed.
• It is not about Stuffing
Entities.
• It is about Creating a
Knowledge-base in the form
of Content.
120. Google didn’t give up.
“The destiny of [Google’s
search engine] is to become
that Star Trek computer, and
that’s what we are building.”
- Amit Singhal
121. Google didn’t give up.
“Google is
designed for
users, not for
websites.”
- Lawrance Page
122. How to Understand Language Models and
Semantic Connections within them
‘Purple Yam’ -> ‘Sweet Potatoes’
123. How to Understand Language Models and
Semantic Connections within them
‘Breville Juicer’ -> ‘Product’ + ‘Book’ Search