Transform the following transcript from a video course on semantic SEO into comprehensive, detailed notes.

- Eliminate filler words, casual language, and any irrelevant content.
- Focus on extracting key points, actionable strategies, definitions, and examples related to semantic SEO.
- Organize the notes into clear, logical sections based on the video's topics, using headings, subheadings, and bullet points where appropriate.
- Ensure the final notes are concise, well-structured, and easy to follow.
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Fundamentals of Semantic SEO
 Understanding and Grasping the Essence of the concepts below is a must for understanding all the lectures in the fundamentals section. Any concept below is not something that you have seen before, including Topical Authority, and Topical Maps.

 

When we use the concept of Topical Authority, or the Topical Maps, we do not mean "focus on topics, not keywords", or "topics in SEO", or "content planning", etc. The phrase and concept of Topical Map are directly used for the first time by me, even if I have seen cheap teachings around the concept, I couldn't see a proper topical map in over 2 years. If you watch the testimonial videos that we have published, and will be publishing, you will realize that most people who come to one-o-one training understand the essence of Topical Maps after spending 5–6 hours together. Thus, you will need to watch some of the recordings multiple times, and you will need to examine the free-training that I published over the past 2 years to get mindset support.

 

When it comes to the phrase topical authority, it is used by Google for the first time inside the Agent Rank Patent, but it is used for authors in an answer organization system, it is not used for websites. I didn't find or invent the "phrase," but I have given it a new definition. If you do not understand this definition, you will miss its essence.

 

"Topical Authority is ranking over an authoritative website for a certain amount of time with a lower cost-of-retrieval, higher accuracy, clarity, and information responsiveness by creating semantically organized content networks in the form of main and supplementary content by optimizing micro-macro semantics and contexts."

 

If you do not know what "main content", or "supplementary content", "content configuration", "micro-macro semantics", "macro-micro contexts", and contextual domains, knowledge domains, entity-attribute-value (EAV), knowledge-base, a semantic content network, and other related concepts mean, it means that when I say "topical authority", you think something else, and I say something else. If we can't communicate with the correct definitions that are bound by phrases that create the concepts, we can't progress.

 

Lastly, this course is not about using an SEO tool better, it is about using your brain better. Your brain is already semantically perfected by God/Gods, or/and History of Evolution, thus, trust reason, and logic while learning lectures, you will be learning your own brain reflexes. You will discipline your mind, and you will have a structured thought-stream to define, associate, and communicate.

 Topical Authority is a state as formulized in "Topical Coverage * Historical Data".

Most SEOs think that they understand this definition easily, but they do not.
For example, what is Historical Data?
Which website below has higher historical data?
A website with 2 years of ranking history, or a website with 10 years of ranking history?
What if the website with 10 years of ranking history has just 1 session, while a website with 2 years of ranking history has over 10,000,000 sessions.
Historical Data is not about "time", it is about "user engagement" and the quality of this "engagement".
A mouse-over, impression, and ranking in the 94th ranking is included in the historical data.
Non-quality user-engagement or query session logs can demote your website after a certain amount of time.
If you lose your rankings today, it is because of the historical data from 6 months ago.
It means that your current state comes from at least a half year ago.
Cleaning the bad historical data for a website, requires a good historical data with a stronger signal.
What is Topical Coverage? Is it just opening a new web page for every topic, or asking a question for every entity?
Learn this, Stuffing Entities are not providing higher rankings.
Stuffing attributes won't provide higher topical coverage either.
Topical Coverage is not measured by the number of web pages, or mentions of entities.
If you didn't define X, it means you didn't cover it.
If you didn't connect X to Y, it means you didn't cover it.
If you didn't match the Macro-context of the web document to the Query Context, it means you didn't cover the entity inside the query with the proper context.
If your definition misses certain types of aspects, it means you didn't cover it.
To be able to rank higher for the "Electric Scooters", defining the "range", or "charger" concepts are needed.
Telling "battery quality" or "battery charge duration" is not good enough.
Going for operating temperature, safety features, voltage, capacity, cycle life, discharge rate, size and shape, energy density, warranty, brand reputation, maintenance, durability are needed.
And, going for durability (of the battery) against vibration, wear and tear, exposure to water, shock, overcharging, charging frequency, age, bad storage conditions, humidity, and high or very low temperatures is needed.
And going for durability of the battery of an electric scooter based on its chemical composition requires going for lithium iron phospohate (LiFePO4), Nickel Cobalt Alumunium Oxide (NCA), Nickel Manganese Cobalt Oxide (NMC), Lithium Nickel Cobalt Aluminum Oxide (NCA), Lithium Titanate Oxide (LTO), Lithium Polymer (LiPo), Nickel Iron (NiFe), Zinc-air (Zn-Air), Sodium Nickel Chloride (NaNicI).
Going for battery voltage, operating voltage, voltage regulator, charging voltage, peak voltage, voltage display, voltage stability, voltage conversion (motor voltage), nominal voltage, and charging voltage is needed.
So, will you open new pages for all these? Or will you include all these on a single page?
If you include them on a single web page, how will they be ordered?
What questions should I ask, and what answers should I write?
Or what prompts to use, and how to train AGI to process these with the correct context.
If you process LifePO4 with Electric Scooter directly in your H₁, you will be diluting your context.
If you include it in a different macro-context, you will be expanding your topical map.
But where a topic starts and end? Or whether these borders change, or are they fixed?
If a competitor manages to change the contextual borders of a topic, does it mean that your topical coverage is decreased despite you cover the same amount of contexts?
Even if you process all these battery materials, if you do not connect them back to the battery related attributes, you will be diluting your context, and topical consolidation.
Even if you process all these battery materials, if you connect them only to electric scooter batteries, still you will be diluting the context, even if you increase topical consolidation.
Topical Coverage is determined based on the complete-comprehensive and structured process of information on web documents that are designed based on possible and related search activities.
If I talk about possible and related search activities, it will be a little black-hat.
But if we continue from the "complete-comprehensive and structured process of information", it means that an entity is processed with every attribute, for any possible connection, by completing every possible factual relevance, and processed in a web document for higher Relevance and Responsiveness.
Relevance and Responsiveness are not the same.
Relevance is for improving Information Retrieval Score which comes from Term-weight Calculation, and Reading the Retriever's mind with math algorithms.
Responsiveness is a direct Information Extraction Process, and it requires a direct answer that satisfies the possible or related search activities.
For the query "Cancer", the possible and related search activities might be "learn cancer", "go cancer treatment", "compare cancer treatment methods", or "cancer treatment center comparison", or millions of different combinations can be created.
The Represented and Representative Queries are concepts from Query Processing.
For a single-word query such as "cancer", the distribution of probabilities comes from query variations, search behaviors, and query search demand, which creates Query Semantics.
To understand the Relevance, the web document has to be relevant to all the possible interpretations of the single-word query.
To provide the Responsiveness, the web document has to satisfy all the possible needs behind the query.
But, search engines interpret queries not just based on query semantics, sometimes they assume the highest PageRank, or most authoritative source interpreted the query correctly, and they bring SERP the most similar other web documents.
The Semantic Distance between the queries change continuously, thus during and after the Broad Core Algorithm Updates (BCAUs), the SERP characteristics (SERP Features), and ranking web source and document types change tremendously.
To increase the Topical Coverage, a web source shouldn't go for irrelevant topics without completing the previous one.
The quality of a topical map, and a semantic content network (collection of connected web documents that are semantically optimized for a topic) can affect the rankings of the web source for another topic.
The non-quality pages will cause other quality pages rank lowered.
Topical coverage is the collection of related topics with a specific contextual connection, and a knowledge domain that involves all the related entities, and attributes with accurate verbalization in a web source's content network.
To consolidate the topics under a context, and increase the contextual relevance of the web source, the topical coverage should be increased with the documents that are optimized with macro and micro semantics with macro and micro contexts.
For example, a document with "20 Facts about Lithium Iron Phosphate" can't be connected to the Electric Scooter directly, the association here is weak because search engine doesn't have query sequences, paths, or documents that mention these two together, thus it can't trigger a new inverted index, or bring other inverted indexes together.
Thus, it requires processing, batteries, battery types, batteries for electric scooters, and battery production, battery materials separately from each other. To consolidate the context further, comparing batteries of electric scooters to the electric bikes, and calculating the distance difference between electric scooters to the electric cars, and electric scooters to the electric bikes is needed.
Mentioning the word "battery" in a simple way, and stuffing 99+ sponsored links to the web documents means that this is a sponsored web source, and every mention of every concept, whether a brand, product, or product dimension, is paid.
Using a properly constructed knowledge base in the form of a semantically optimized content network is needed to increase topical coverage properly.
The Macro Semantics involve the overall characteristics of a content network from the point of view of semantics.
For example, what are the site-wide N-grams that can appear on a website?
What are the most used nouns, adjectives, or predicates site-wide?
What are the most commonly used question formats site-wide?
What types of queries are targeted overall? Do they start with what, or a superlative word?
How are the heading vectors are constructed?
What are the first words of the paragraphs? Do they answer the question, or do they use rhetoric without information?
Do they link to other pages without giving a single answer?
What are the heaviest context terms in the web documents?
Do context terms match the query context?
For example if a search engine interprets a query like "movie 20", as the "best 20 movies", or "movies for 20 years old", or "The Movie called 20", or "Movies from 20s", or "Movies for 20s", the web document will need to distribute the probabilities, and arrange the contextual flow from macro context to the micro contexts.
Imitating the SERP always won't solve the problem, but Understanding the Reason behind the SERP will.
Micro Semantics involve the sequence modeling, word-by-word optimization of the documents for a higher relevance and responsiveness.
It involves the optimization of the visuals with subject and object entities.
The subject entity is the most concrete and tangible object in the visuals.
Object entities are interpreting the context of the image by changing certain interpreations. Like, "a fish and a bear", or "a lake and a bear".
Sequence modeling is a term from Natural Language Processing, that involves changing the word sequences for higher responsiveness and contextualization.
For example the "Teacher yelled students", and "Students are yelled by Teacher" word sequences do not distribute the relevance in the same way. In the first one, "teacher" is heavier, and in the second one "students" are heavier for the contextualization.
The predicates are important to signal the overall context, the predicate "yell" annotate a different context from the "shout", and all the probabilities of word sequences change.
Micro-semantics are heavily important, because right now, we are already creating websites by downloading the knowledge bases and verbalizing them with LLMs, and optimizing an LLM with micro-touches creates huge relevance and responsiveness differences.
Main Content is the main part of a web document.
It involves all the context-terms, topical entries, and main entities inside the web document.
It gives the main context-flow, and coverage while also including a summary of the entire article.
It doesn't touch sub-contexts, or minor topics.
It processes the macro-context of the document and provides a proper connection to query contexts and semantics.
It doesn't involve too many internal links, unless it is inside the macro-context.
Supplementary Content touches the micro-contexts, sub-topics, and it provides internal links to the side-topics.
Supplementary content is used to provide a better association, and "Neighborhood Content" between different segments of the topical map.
The supplementary content is always connected to macro-context of the web page, but it processes it with a connection to another macro-context.
Topical Map is a concept and phrase that I found, used for the first time during the interview with Matt Diggity. The main problem with the concept is that it was a simplification, thus it is even more simplified. I have seen people use the concept of topical maps as connections between entities via attributes, or blog post names to process without any understanding of context, or semantics. To create a proper topical map, you have to understand the concepts below.
Source Context is the purpose of the source (website, and web entity (CEO, Social Media Platforms, and collections of other web aspects of brand)), how the brand monetizes its content, and how it turns search engine users into customers and clients.
Source Context has to be connected to the Central Entity with a proper attribute.
For example, if the source context is "Visa Consultuncy", certain types of web components such as "input elements" signal conversion points during the HTML Normalization.
And, concept of "Visa Consultuncy" has to be united to any other aspects of a "country", whether it is about country's culture, religion, geography, climate.
If the source context is "Real Estate Consultuncy", the "country" attributes will be connected to the "Real Estate Investment". The climate of the country will be processed for types of properties to invest in, or the climate's effect on real estate investment, not for the best time to visit a country.
The macro-context of the "climate" node has to be on "climate", but the micro-context and supplementary content connections will change according to the Source-context.
The Source Context is connected to the site-wide N-grams, the source's context has to be reflected on the website on every web page, in boilerplate, and in the main content as well.
Core Section of the topical map is the unification of the source context with the central search intent. Central search intent is the intent that will be reflected site-wide, and all the sections of the topical map and the semantic content briefs whether in the macro-context, or in the micro-context areas.
Core Section of the Topical Map focuses on a specific main attribute of the central entity.
The specific attribute of the central entity comes from the source context.
For example, if you are an affiliate for electric car chargers, the "quality" is the main attribute, and "durability, charge time, or maintenance" are the "derived attributes" from the main attribute.
For example, if you are an engineering company for electric car chargers, the "production" is the main attribute, and "materials, designs, types" are the derived attributes.
According to the Source Context, the Core Section of the Topical map has to be densified further.
Outer Section of the topical map is to improve the overall historical data (explained at the top). It is to increase overall topical relevance, and contextual consolidation of the web source for the specific entity.
The outer section of the topical map focuses on the minor attributes of the entity, not the main attributes.
Outer section of the topical map propogate the trust, and quality signals to the core section of the topical map with links or linkless connections.
For example, if it is for "Visa Consultuncy", the core section of the topical map will focus on the "Visa" attribute, while the outer section focuses on all other attributes for a country, whether "religion", or "language schools".
For example, if it is for "Pension and Retirement Planning (401k plan, ROTH IRA, Required Minimum Distribution, Catch-up Conditions, Long-term Care Insurance, Reverse Mortgage, Individual Mortgage, Other sub-types, or alternatives to it)", the core section of the topical map will be about "Retirement" under the context of "Financial Independence", and the outer section will be focusing on all Financial Aspects and Elderly Life (Elder Law Attorney, Power of Attorney, Estate Planning, Gifting, Annuity, Medicaid, Retirement Community).
For example, if it is for the "Divorce Lawyer", anything related to the predicate "divorce (mediation, trial, property division, separation agreement, alimony, petition, child custody, support)", and "legal (grounds, prenuptial agreement, default judgement, emancipation, temporary orders, annulment, discovery, marital property)" context will be in the main part of topical map, and anything related to marriage (statistics, relationship mistakes, types, dynamics), and roles (any adjective that can be used for husband, or wife, or any other term like child), or marriage functions (financial partnership, companionship, communication, household responsibilities, physical intimacy, emotional support) in marriage will be in the outer context.
Central Entity is the entity that appears in every subsection of the semantic content network whether in main content and macro context, or supplementary content and micro context.
Central Entity is the entity that gives its main and minor attributes to the core and outer sections of the topical map.
Central Entity always appears inside the anchor texts with a synonym value.
Central Entity and Source Context are united together to create a connection and major focus on the website content for creating a connection to the users' possible and related search activities.
You can have the best web page, and best possible content for "cancer treatment", but if you are not a doctor, search engine won't rank you, because users won't be able to perform its end-goal.
Thus, reflecting possible and related search activities with proper web page layout and web components is needed.
You can have the most relevant and highly responsive content, and all the PageRank for the query of "credit calculation," but if there is no calculator on the page, you can rank for "credit calculation formula", but not for the "cheapest loans" or "lowest interest rate loans".
If there is no application form or possibility, you can't rank for highly commercial queries.
Sometime search engines replace aggregators with direct instutions that users can apply, because it decreases the counts of clicks, and web page count for the final "search behavior" which is connected to "real world behavior" (another time we can explain this further.).
Central Entity determines the direction of the topical map for being classified properly with the authoritative sources to outrank these sources in the future BCAUs.
Central Search Intent is the intent that will appear in all the topical map, and semantic content networks whether it is in boilerplate, or main content.
The central search intent is the unification of the source context with central entity.
The central search intent is heavily processed in the core section of topical map.
The central search intent is reflected on the outer section of the topical map.
Content Configuration is the process of changing, and updating the existing content according to the changed semantic distances, or similarities, and increasing the relevance and responsiveness continuously.
Publication Frequency (Momentum) is the frequency of major content updates, or new content publications to take the attention of the search engine to be prioritized for being crawled, indexed, and ranked earlier and higher.
Contextual Coverage is the context that covers a certain portion of the web page.
If a context is heavily and vastly processed, it will dilute the prominence of other sections.
If a context is processed lightly, but connected to the certain interpreations of the query, it will decrease the relevance.
Contextual Flow is the order in which contexts are processed.
Processing the same things with different order will create different possible click satisfaction scores.
Heading formats, heading words, or heading hierarchies change the context's priority.
Contextual Hierarchy is to adjust the weight of a context's coverage.
Contextual Hierarchy is represented with the typography, visuals, coverage of the specific sub-section of the web page.
Contextual Hierarchy changes the weight of an internal link for relevance and PageRank pass.
Contextual Hierarchy changes the macro-context of the web page.
Contextual Border is the border between macro-micro context section of the web page.
It provides a slow transition from main content to supplementary content.
It provides a grouper question to deepen the main context while connecting it to other side-topics.
Contextual Bridge is the connection between two different topical map node.
It is provided by aligning and consistent information without a link.
It is provided with a hypertext for providing contextual connection between two different topical map nodes.
Vastness-Depth-Momentum is a simplification that I do for all these things. Basically it means, go wider, go deeper, go faster.
If you can't create a "wide source", go even deeper and faster. If you can't go faster, create way much more wider and deeper.
It means that whichever is missing from out of these three, you have to complete its missing effect by improving another.
You can create a bigger topical map, or really deep content briefs for a small topical map.
You can publish lots of things quickly, or you can publish deep, comprehensive and complete web documents for a topic slowly.
 

There are many other concepts to process under the context of Topical Authority. Thus, when I say Topical Authority, and Topical Maps, the thing that I invented and coined is too different from what you thought before. And, unfortunately until this very moment, it wasn't possible to explain these publicly. Most of these concepts will fit into your mindset when you watch the lectures multiple times. Whenever you watch these lectures, you will learn something new, and realize something that you didn't realize before.

 

This course was not created for quick hacks, shiny things, and I never offered easy things. SEO is a highly complex discipline, and thanks to Artificial General Intelligence, it will be way more complicated with the rise of new search engines.

 

"This is not an upgrade for your SEO Toolset, but it is an upgrade for your Mindset.
The course is for leveling up your brain capacity, and teaching you new brain reflexes, and thought streams while improving your conceptualization skills, and awareness of the surrounding semantic world."

Koray Tuğberk GÜBÜR

 

A quick conceptualization table for the concepts is below.

 

Concept

Explanation From Me

Micro Semantics

Process of optimizing sequences of words, phrase co-occurrences, and sentence structures for increasing relevance and responsiveness. Includes discourse integration optimization, and sequence-to-sequence connections for changing contextualization.

Main Content

The main part of the web document that processes the specific context-terms, and topical entries. It satisfies the major query needs, and touches on the minor query needs with the contextual border. This is where the macro-context is processed.

Supplementary Content

The supplementary part of the web document is where the micro-contexts, minor entities, and attributes are processed in a connected way to the macro-context and main content. It involves more internal links and contextual bridges with links or linkless connections.

Topical Map

The topical map has the core and outer sections to process the central entity with the main and minor attributes. A topical map is intended to provide a better topical coverage by focusing on all the query networks and query contexts in these search terms, increasing site-wide relevance and responsiveness with higher historical data and quality session logs.

Source Context

The purpose of the brand identity, and how the brand monetizes its content. It represents the main focus of the website, and its connection to the central entity.

Central Entity

The entity that appears in the core and outer sections of the topical map, and all the site-wide n-grams, and context-terms are related to the central entity. It is covered site-wide, and every topical map node, from main or supplementary content, has to touch it.

Central Search Intent

The central search intent is the unification of the central entity and the source context. It is processed in both of the outer and core sections of the topical map. It is connected to the macro-context, and micro-context of every content brief, and every part of the semantic content network.

Content Configuration

Process of optimizing relevance and responsiveness continuously according to the changed semantic distances and similarities of the query terms. It comes from changed query semantics, and it requires refreshing and re-configuring the topical maps. Most of the time, if the web source is already topical authority, it means that configuration will be needed from the competitor side, mostly.

Publication Frequency

The momentum. The purpose of publication frequency is to be prioritized for crawling rate, and indexation, it is prioritized for higher rankings, and taking the attenion of the search engine content exploration systems.

Contextual Coverage

The number of web page document section for a specific context compared to the other sections. It can dilute the relevance or increase the relevance. It is connected to the Passage Indexing (Ranking), and it is needed to not break the Contextual Vector.

Contextual Flow

The order of the processed contextual vectors that represents contextual domains and knowledge domains in the web document. Order of headings, questions, or paragraphs change the relevance of a document. It determines what is processed under what circumstances. It changes the weight of the sections, and the contextual connections.

Contextual Hierarchy

Connected to the Contextual Flow. It determines which contextual vector comes before which one. The order of the contextual segments on a web document changes the overall connection to the search terms.

Contextual Border

The border between the macro-micro contexts, and main and supplementary content sections on a web document. It provides a transition from major query needs to the minor query needs with further associations.

Contextual Bridge

Hypertext, or linkless connections in a semantic content network. It helps semantic content network to complete itself with different part. It focuses on query paths, sequences, and correlative queries.

Vastness-Depth-Momentum

A simplification to explain everything in a semantic content network, and a topical map for gaining topical authority.  Go faster, go deeper, go wider. If one of these is missing, complete its missing effect by increasing another.

Topical Authority

Ranking higher for a certain amount of time compared to other web sources with higher clarity, accuracy, information quality, and responsiveness for a cheaper search engine evaluation and retrieval process so that the web source is preferred by the search engine for certain topics for prioritized rankings. Basically, it is not "focus topics, instead of keywords, or SEO in topics". Learn concepts properly, and credit the original source.

Historical Data

Not time.

Amount of user engagement for a certain amount of time.

Fresh historical data is more important than older historical data.

There are negative, neutral and positive historical data types according to the behaviors of the users, whether it is a hover-over, click, touch, text-select, clickless search, etc.

Topical Coverage

Requires creating a topical border to calculate the topical coverage.

It involves the connected attributes, knowledge domains and contextual domains for a specific query network.

It can't be increased by stuffing entities, attributes, or opening a new page for everything.

Processing every macro and micro context for a knowledge domain by connecting and associating every entity via their attributes, including all the content-formats, with proper web page layout and components for signaling the related and possible search activities that come from the query context, aspect, and definition based on query semantics.

Relevance

The Information Retrieval Score that comes from certain text processing methodologies such as term saturation, length normalization, co-occurrence matrix construction, BM25, TF-IDF, GlovE, Word2Vec and more. It shows overall connection, but it is still the Blind Librarian state of the search engine.

Responsiveness

The Information Extraction Process of the Search Engines for giving the direct answer, even if there is no relevance, there might be a quality answer. Thus, it requires query-question-answer pairing, and indexing. Thus, it is connected to Passage Indexing (Ranking).

Represented and Representative Queries

A search term has representative and represented query versions for expressing itself.

This is not what you would call "seed query", or "tail queries".

Search engines do not give the full weight of relevance to the terms that are put into the search bar. The relevance of the terms in the search bar is distributed based mainly on the representative queries. For example, the queries "board vision", and "vision board" can give different results, even if they mostly mean the same thing, because there will be different contextual connections, and query interpretations for both of them.

This indicates that the representative query has access to the represented query's relevance.

Topical Map

A content network design based on semantics for achieving the topical authority state by including overall macro contexts and semantics with publication frequency.

Contextual Relevance

Contextual appropriateness and significance of terms for the given context to reflect the meaning of the term in that context.

The noun love might have a "sexual, or religious" context.

Macro Semantics

Macro Semantics is the semantics for the main and prominent parts of the web documents' relevance and context whether it comes from headings, anchor texts, site-wide n-grams.

Micro Semantics

Micro Semantics is the semantics for the micro-adjustments for context and relevance, whether it comes from a single word change, punctuation, or word-order.

Understanding SERP

A quick phrase to annotate the process of understanding the search engine reasoning behind the results for query search terms.

Quick Definitions of the Concepts

 

Use the references below to understand the lectures and the concepts better and deeper.