Semantic SEO Course for Topical Authority

To be able to use Semantic SEO, one needs to learn Semantic Search Engines, and how a semantic search engine processes a query. What Query Semantics and the Lexical Semantics have the prominence to understand the Semantic SEO.

Semantic SEO is to provide a better contextual relevance, structure, connection, coverage, and flow within a content item and content network. A semantic Content Network is created to provide an order of facts, accurate triples, and relevant co-occurrence matrices.

To use Semantic SEO, one has to learn the processing of the information on the semantic web based on semantic web patterns. Using methods of semantic SEO methods define the ways of creating a semantic information network via the map of concepts and connections of entities. To use the Semantic SEO, having a Semantic SEO Course can provide four year of learning within under one year for someone with search engine understanding experience.

Yes, it is hard to understand Semantic SEO. The main obstacle to understanding Semantic SEO is the necessity of reading the Google Patents, Linguistic Research, and examining the Search Engine Result Pages with the semantic understanding. Learning Semantic SEO and Understanding the Linguistic preferences of the Search Engine requires empathy with the search engine designer and the developer (engineer). To have that empathy, a Semantic SEO might need two years of experience.

To extract and generate questions in Semantic SEO for a topic, one has to use the question generation and answer pairing methodologies of the search engines. For example, the Keywords to Questions, and Candidate Answer Passages are the main research from the semantic search engines for generating questions to pair questions with answers. The first 4 lectures of the Semantic SEO Course focus on this subject deeply.

To analyze competitors' sitemaps for Semantic SEO, the competitor's sitemap is scraped and filtered based on the URL structure. Crawling the URLs in the sitemap of the competitor is also beneficial to provide a better understanding of their topical map and content network. Finding the "wholes" between their semantically broken content networks is a necessity to create a better relevance and quality signal.

Topics and Topical Maps are the fundamentals of Semantic SEO. Thus, every topic targets different types of sub-topics. To estimate the article and the web page numbers that are necessary for a topic, the topical analysis is done by Semantic SEO. A topic with more than 5 different sub-topics and different phrase taxonomies within the queries requires more web pages than a topic with only 1 sub-topic. Another factor for estimating the content need for a topic is understanding of the interest areas of the users so that the web entity can create a better query session log data for a competition. The competitors' content amount and their historical data affect the content network's success chances.

Internal Links are the Semantic Annotations with Contextual Connections in Semantic SEO. An internal link can pass relevance, PageRank, and the document purpose. Internal links are used to prevent cannibalization between the documents. A search engine uses the internal links to find the new internal documents based on relevance. A proper crawl path signals the relevance of the documents. In Semantic SEO, the internal links are used with mutual coverage with an understanding of main and supplemental content. The practical lectures on semantic SEO process the internal linking for Semantics.

Existing Content and the newly created content pieces are connected to each other based on the specific contextual connections. Internal links carry a value for providing a new semantic content network by completing the existing content network, whether it is organized with semantics or not. Creating "revision content briefs" and adding new content pieces to the website is necessary.

The probability of the Failure to Learn Semantic SEO is high for a new person for Semantics. The main problem with the Search Engine Optimization industry is that most SEO agencies and publishers think that anyone can do SEO. Becoming an SEO is a hard, long, and expensive journey. Learning a vertical of SEO such as Semantics requires high dedication and patience.

An example to explain why Semantic SEO is hard to learn and why the probability of failure is high is below.

A wrong anchor text can change the anchor text index of the specific web page, and a wrong index of anchor text can dilute the relevance of a specific document. This specific document can decrease the satisfaction possibility of the other documents. A technicalSEO, or a traditional content marketer can't see these types of nuances.

There are thousands of different SEO Factors in Semantics that are connected to each other. Thus, writing a few titles and ranking a few HTML Documents don't make you an SEO while writing a few liens of code and creating a simple HTML Document don't make you a developer.

No, keywords are not necessary for Semantic SEO. In Semantic SEO, there are only "queries", and "phrases", and their connection from a search bar to a result resource. Queries and keywords are not the same things. Queries are the phrases in the search bar, while keywords are the words used to define a document or design. Thus, using the concepts and the terms correctly is crucial to understanding Semantic SEO.

To implement the Semantic SEO for small markets, industries, and niches, the Topical Map and the Semantic Content Network have to be smaller but better consolidated.

Understanding the topical borders and neighborhood topics is a must to understand the necessity of the deepness and vastness of a topical map.

To extract and generate questions in Semantic SEO for a topic, one has to use the question generation and answer pairing methodologies of the search engines. For example, the Keywords to Questions, and Candidate Answer Passages are the main research from the semantic search engines for generating questions to pair questions with answers. The first 4 lectures of the Semantic SEO Course focus on this subject deeply.

To analyze competitors' sitemaps for Semantic SEO, the competitor's sitemap is scraped and filtered based on the URL structure. Crawling the URLs in the sitemap of the competitor is also beneficial to provide a better understanding of their topical map and content network. Finding the "wholes" between their semantically broken content networks is a necessity to create a better relevance and quality signal.

Topics and Topical Maps are the fundamentals of Semantic SEO. Thus, every topic targets different types of sub-topics. To estimate the article and the web page numbers that are necessary for a topic, the topical analysis is done by Semantic SEO. A topic with more than 5 different sub-topics and different phrase taxonomies within the queries requires more web pages than a topic with only 1 sub-topic. Another factor for estimating the content need for a topic is understanding of the interest areas of the users so that the web entity can create a better query session log data for a competition. The competitors' content amount and their historical data affect the content network's success chances.

Internal Links are the Semantic Annotations with Contextual Connections in Semantic SEO. An internal link can pass relevance, PageRank, and the document purpose. Internal links are used to prevent cannibalization between the documents. A search engine uses the internal links to find the new internal documents based on relevance. A proper crawl path signals the relevance of the documents. In Semantic SEO, the internal links are used with mutual coverage with an understanding of main and supplemental content. The practical lectures on semantic SEO process the internal linking for Semantics.

Existing Content and the newly created content pieces are connected to each other based on the specific contextual connections. Internal links carry a value for providing a new semantic content network by completing the existing content network, whether it is organized with semantics or not. Creating "revision content briefs" and adding new content pieces to the website is necessary.

Backlinks are the links that come from external sources to provide a reference value. Forward links are the outgoing links from a website to another website. Using semantic SEO without backlinks is not an obligation. Semantic SEO and Initial Ranking along with the Re-ranking are connected to each other. To be able to rank documents better and faster, a third-party reference by signaling the authority can help search engine algorithms to be sure about the accuracy and expertise of the website. In this context, a new website, or an old website can have an extensive amount of traffic without backlinks. Search engines focus on internal or endogenous ranking factors. Semantic SEO provide the endogenous ranking factors and signals.

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Crawl Budget is the "Quota of Crawl Hits" from a search engine to a website. Crawl Quote is used by the ads and organic search engine bots at the same time. Semantic SEO increases the content depth and content count of a website based on semantic connections and necessities. Thus, the crawl demand of a website increases via Semantic SEO learnings and implementation. Increasing the crawl quote of a website increases the query count, impressions, clicks and the historical data along with the topical coverage. A website provides new and valuable information decreases the "cost of retrieval", thus a website with valuable new content is indexed faster and ranked better over time by the search engine. Providing a semantic content network with high precision and better informative coverage is a necessity for the websites that demand more crawl quota.

To teach authors Semantic SEO, the training sessions for writing based on the semantics are done. Semantic SEO learnings are taught by authors via practical lessons. Using a code language for the phrase, question, answer, and web page patterns are to provide a standardization of the writing in an algorithmic way. Algorithmic authorship and the Semantic SEO are connected to each other in the context of author education. A semantic SEO creates an algorithm for semantic content network creation. An algorithm that is created for certain types of questions, answers, queries, and documents generate a different contextual consolidation. Thus, the algorithmic authorship is taught to authors via Semantic SEO writing training regularly.

Contextual Sharpness and Context Specifying are the processes of providing a context vector to a search engine for information retrieval and extraction algorithms. Context vectors occur from term vectors, and term vectors occur from word vectors, and word vectors occur from character embeddings. The chain of vectors create the contextual domains and layers. To prevent the contextual dilution, the concepts below should be understood.

• Contextual Domain
• Contextual Layer
• Knowledge Domain
• Semantic Distance
• Semantic Similarity
• Contextual Sharpness
• Context Specifying

Semantic SEO and Context relevant terms are connected to provide a better ranking performance.

Semantic Relevance is the relevance between two concepts. Lexical Relations is used to provide semantic relevance. Two entities are connected to each other via ontologic connections. Concepts and entities are the subjects of semantic relevance. Semantic Relevance, Hypernym Member, Hypernym Path, and Semantic Dependency Tree concepts are connected to see when to divide content into two pieces.

To divide content into two pieces, the two different pieces of the content item have to focus on different subjects of the same object. Internal links have to be determined between them from the correct link texts and positions. Dividing content into two pieces affects the new websites and the authoritative websites differently. An authoritative source can cause a cannibalization by dividing content into two pieces because the "Relevance Radius" and the "Rankability" of the source are better. Thus, Semantic Relevance is leveraged by the new websites since new websites don't have any authoritative historical data, providing a better Information Retrieval Score, and Query Coverage along with Entity-oriented Relevance are advantageous.

To understand when to divide content into two pieces based on Semantic Relevance, the concepts below are necessary.

• Semantic
• Similarity
• Semantic
• Relevance
• Lexical Paths (Hypernym Path, Hyponym Path)
• Lexical Members (Hypernym Member, Hyponym Member)
• FrameNet and WordNet
• FrameNet Parsing
• Word Similarity
• Relevance Radius
• Rankability
• Contextual Vector
• Term Vector
• Word Vector
• Character
• Embeddings

Semantic Relevance is a fundemental concept for Semantic SEO. The sentiment of a sentence affects the relevance of a sentence to a specific query based on context-sensitive ranking. A query reflects a need, and sometimes, a sentiment. An entity is relevant to an event, and an event is relevant to a sentiment. Especially, in the News SEO, the specific named entities are relevant to the different sentimental events. Thus, a search engine ranks the satirical articles, or opinions for specific events. Even if a neutral sentimental article is more relevant than a sentimental article, based on the context-sensitive ranking, a query sentiment matched with document sentiment is ranked better.

Thus, matching the angle of the content, and tonality of the content are necessary steps in the semantic SEO. In the "reviewable entities research", Google search engine suggests using positive and negative feedbacks with proof. Instead of using too deep sentimental power words, use sentimental phrases for Semantic SEO by providing proofs for the declarations of the content.

Part of Speech Tag is the process of parsing a sentence into smaller pieces to understand the roles of the sentences. Part of Speech Tag is a sub-process of Natural Language Processing. Part of Speech Tag (POS Tagging) is a necessity for the Semantic SEO to see which sentence structures and phrases are used by the search engine for Fact Extraction. The Fact Extraction algorithms use Semantic Role Labeling, and SRL is connected to the Part of Speech Tag. In the Semantic SEO Course, the Semantic Role Labeling and the Snythatic Analysis of sentences with POS Tags are processed deeply.

Heading Vectors are the pairs and the flow of headings with a hierarchy. To provide a better Heading Vector with Semantic SEO provide a better Search Intent Covreage along with Contextual Coverage. To choose a better Heading Vector, the concepts below are relevant.

• Contextual Vector
• Context Flow
• Contextual Coverage
• Search Intent Coverage
• Canonical Query
• Canonical Search Intent

Understanding how to cover a topic, and how you cover a topic are different things. To understand how to cover a topic, the SERP/Query Mapping with Autocompletions, PAA Questions, Related Search Terms, and SERP Character along with the competing sources, documents, and Linguistics with Semantic SEO understanding are used. Creating a Topical Map is one of the fundamentals to cover topics within Semantic SEO. To cover a topic in a correct methodology, the topical map nodes, the core section of the topical map, the side section of the topical map, and other topical map-related concepts are used. To use the Topical Map-related concepts, learning Semantic SEO thoroughly is a must. 

To understand whether you cover a topic correctly, or not, the effects of the internal links, related search term changes on the SERP, user flow, correlated and sequential query logs are used. The concepts that are relevant to see how to see in Semantic SEO whether you cover a topic correctly or not, the concepts below are relevant.

• Core Section of Topical Map
• Side Section of Topical Map
• Correlated Queries
• Sequential Queries
• Interest Areas
• Connected Concepts

The signals that show a website processes a topic correctly are listed below.

• Initial Ranking Increase
• Re-ranking Performance Increase
• Crawl Hit Increase
• Query Count Increase
• Impression Increase
• Featured Snippet Increase
• People Also Ask Question Shifts
• Autocomplete Shifts
• Related Search Term Shifts

A website with topical authority affects the search engine's perspective. Thus, changes on the SERP for new features or new elements such as different related search terms are signals that show the website processes the things correctly.

Finding the Relevant Entities for Semantic SEO have two fundamental methods, ontology and taxonomy analysis. Inferring Attributes from Queries, and Understanding Query Definitions along with Query Aspects are relevant. To find the relevant entities with Semantic SEO Understanding, the concepts below are relevant.

• Interest Areas
• Connected Concepts
• Targeted Audience
• Related Search Activities
• Possible Search Activities
• Contextual Domains
• Contextual Layers

The related search activities and possible search activities, contextual domains, layers, and targeted audience are relevant to finding the most relevant attributes of the entities. In Semantic SEO, to find the relevant entities, some of the fundamentals below are used.

• Using Google Knowledge Graph API for Related Entities.
• Using Programmable Search Engine to See Different layers of the SERP.
• Auditing the Link Texts of the Competitors
• Taking the Most Used Nouns of Competitors
• Taking the Most Used N-Grams in Questions from SERP

Interest Areas for Semantic SEO is connected to the Relevant and Possible Search Activities along with the Contextual Connections via Sequential Queries and the Correlational Queries.

Interest Areas connect web search engine users to each other via queries, and audience classifications. In the Semantic SEO Course, all the concepts such as interest areas, targeted audience, intended audience, and search activity, real-world activity and more are processed.

Without understanding a search engine with semantics and their concerns, obstacles and main purposes, optimizing a document based on the Semantics is not possible. Use Semantic SEO to create a best possible interest area profile and connection flow.

Interest Areas are the affinities for the users with connected concepts. A connected concept can have multiple interest areas from different audience clusters. An audience cluster with multiple interest area can have different possible, relevant search and real world activities in a connected way.

Semantic SEO is to provide best possible interest-based user navigation for the possible future search activities. A search engine doesn't solely rely on the queries, and their relevance, but also the interest area, and further query-less expressions from the neural networks of humans.

To create a Topical Map as a beginner, the one uses the existing topical maps of the existing websites. To create a topical map is not easy for a beginner, but the relevance consolidation, and the Source's Context are the most prominent concepts for it.

Source Context is one of the fundamental concepts for creating a topical map. Understanding the context of a website is necessary to create a connection between the connected concept, and interest area to the specific website via an entity, or named entity. Thus, Source Context involves the identity, purpose, and relevance of a website to a specific topic. Based on the Source Context, all the Contextual Vector, and Heading Pairs with Subordinate Texts change. In the Semantic SEO Course, the Source Context has been processed deeply.

Zero Volume Queries are prominent for the Information Gain Score, and providing a positive balance for the information gap between the source and the competitors is necessary. Zero Volume Queries are called zero search volume keywords because they do not have any search demand. But, the search volume research technologies are usually mistaken about the real search volume of a query. Thus, most of the Zero Volume Queries are not zero volumed or searched queries.

Using the Zero Volume Queries helps a search engine to create a better contextual consolidation with uniqueness. Writing the same things with a richer vocabulary, and new N-Grams along with the phrase patterns are helpful for a search engine to rank a document. The Document Comparison Models try to eliminate different web pages on the SERP, or index of the search engines. Having unique phrases, an information gap is an advantage for providing a better click satisfaction possibility.

Queries with no search demand can show the unique value of a source on the web. A semantic search engine can generate "synthetic queries" to understand the relevance of different phrase variations to each other to improve the overall quality of the SERP. If a "synthetic query" is relevant to other queries, even if there is no search demand, the search engine calls it "seed query" in its patents, to signal its importance for guidance. Most query demand tracking technologies use "ClickStream" technology, thus they miss the real value of minor-rare queries.

In this context, Queries with no search demand and Queries that look like with no search demand are not the same thing. Even if a query doesn't have a proper search demand, it can increase the relevance of the document to dominant and minor search intents.

Every word has word proximity to another word within a co-occurrence matrix. A zero search volume keyword can improve the phrase-variation count, contribute to the phrase taxonomy, and improve the contextual signals of the document for a cluster of search intents behind the "seed queries."

"Besides this "theoretical SEO" explanation for Semantic SEO, to find "Assumed Zero Search Volume Keywords" and "Real Zero Search Volume Keywords", you can also use the methodologies below.

1. Use the "variable portion" of questions. "What is X for Y in C", "Types of X for P", "Z Principles for installing C without having a P for V" => "What is Love for Women in 40s", "Types of Love for Greek Philosophers", "Socrates' Principles for having a strong love for family".
2. Use "lemmatization" of words. "X for P", "P of X", "X and P", "Xed by C", "Xing", "X's definition based on C" => "Love for Animals", "Love of Animals", "Love and Animals", "Being loved by an Animal", "Love's definition based on Greek Culture".
3. Understand the "theme of words", in other words, "Contextual Domains". If you search these queries, you will see that Google will find a "representative query" for your possible search intent, and they will rewrite your previous query. It means that they understand the "user and query context". In other words, these "phrase variations" can signal the context and relevance.
4. Use your reflexes, since you are a semantic creature. I didn't check the queries above whether have 0 search volume or not, I just made these queries up. So, use your own brain to generate these queries without search demand. But, also you can use Python for it. You can generate "N-Grams" - "biagrams, trigrams and more". Most of them won't have a search demand. You can use lemmatization, and question generation libraries based on text from SERP via Python too. Maybe in the future, I can create some tutorials for these too.

In Semantic SEO Course, the prominence of the Information Gain Score is processed deeply with proofs from the search engine patents and the research papers.

Article Structure is the outline of the content or the content brief. The article structure is processed by the search engines to understand the overall satisfaction possibility of a webs search engine user. Using a "document template" is necessary to craeate a complete article structure. Similar entities, with similar attributes, are searched for similar search intents. To create a complete article structure, every aspect of an entity-query cluster has to be processed in a word count economic way. Answering the possible questions in a direct and well-formatted way is the main obstacle by providing a proper contextual hierarchy.

The parameters that can be checked for semantic SEO article structures are listed below.

• Processing Every Entity for a Topic.
• Processing Every Attribute for Every Entity in a Topic
• Giving Every Article an Order of Facts
• Checking the Internal Link Positions
• Checking the Internal Link Uniqueness
• Checking the Sentence Structures
• Protecting the Question and Answer Formats
• Not Covering a Thing Unnecessarily Too Dep for not Diluting the Relevance of Another Thing
• Use Proper Contextual Hierarchy
• Balance the Contextual Coverage
• Create a Proper Anchor Segment Between Content sub-items

Most of those concepts and the methodologies are processed in the Semantic SEO Course with proofs and practical examples.

Yes, the Silo Page concept is relevant to Semantic SEO. Andrew Houge couldn't name the Semantic Search Engine directly. He used the name "Understanding Search Engine", or "Contextual Search Engine", and lastly the Semantic Search Engine has been used. Thus, Silo Page Concept relies on collecting similar things together in the specific group web page. But, even if the Silo Page is relevant to the Semantic SEO, it doesn't mean that its shallow definition is good enough. A concept for the silo page doesn't include anything for the Query Definitions, Query Aspects, or Entity Attributes and Possible Search Activities. Thus, In Semantic SEO Practices, using the concept Topical Map, and Semantic Content Networks is a more convenient terminology option.

In Semantic Content Networks, there are different types of grouper web pages such as a Root Page for a Topical Map, and a Seed Page for a sub-tropical map.

These hierarchical differences are necessary and come from the semantic search engine's document, query, and user clustering understandings.

Yes, Natural Language Generation can be used within the Semantic SEO. Natural Language Generation is the process of generating text from human language based on the Natural Language Processing and Understanding algorithms. Thus, Algorithmic Authorship with Humans or the machines can be used within the Semantic SEO. GPT-3, or GPT-J and other types of NLP and NLG Algorithms can be used to generate semantically optimized text. In this context, using the customized Transformer Algorithms is better than using the already exploited algorithmic text generators. Because, similar algorithms generate similar texts, and search engine can differentiate the original authors from machine-like authors easily. Not providing enough uniqueness can harm the "Similarity Threshold Score" of the generated content, and it can be deindexed, or depromoted on the SERP.

Natural Language Optimization is the process of optimizing the textual content via Natural Language Generation. Algorithmic Authorship, and Natural Language Optimization are relevant to Semantic Search Engine Optimization. Providing a better sentence structure, or shorter sentences and clear propositions, including the co-occurrent entity pairs with clarity can be improved via Natural Language Optimization.

How Do We Know Which Topics, Entities, and Questions are Needed to be Covered in Semantic SEO?

To to know which topics, entities, questions are needed to be covered in Semantic SEO, the attribute relevance, attribute prominence, and attribute popularity concepts are used.

• An entity can be prominent but not relevant to the Source Context.
• An entity can be irrelevant but regularly trending and indirectly relevant to a specific attribute.
• An entity can have relevance, but its most prominent attribute can be irrelevant.
• An entity can be popular for queries, but it might not have a topical relevance for the specific Contextual Domain.

To use an entity and its attributes, the relevance, prominence and popularity are balanced by the Semantic SEO in the Semantic Content Networks and Topical Map.

The Attribute Qualifiers and the Entity Connections are processed in the Semantic SEO Course with high-level information.

To see whether an entity needs a seperate web page, or it should be an existing part of an existing web page, the Semantic Distinctiveness and Similarity should be understood. The concepts that are relevant to the Entity Explanation with textual content are below.

• Main Entity
• Minor Entity
• Macro Context
• Micro Context
• Contextual Coverage
• Dominant Search Intent
• Query Semantics
• Query Clustering Methodologies

A web page can't have two different macro contexts and two different main entities. Thus, based on the query semantics and the entity prominence along with its attribute's qualifiers, the Semantic SEO can adjust the contextual consolidation, and domain borders.

The main difference between Topical Authority and Topical Relevance involves the difference between being authoritative and relevant. A website can be topically authoritative, but still, topical relevance can be lower. It doesn't mean that every topically relevant source is also topically authoritative. Topical relevance is completing the supporting content items with the understanding of neighborhood content pieces. The Neighborhood Content represents the topically adjacent content items with adjacent search intents and closely related queries. Thus, these types of queries can be targeted via a query-document-intent template model.

By providing better Seed Pages and the Root Pages, a website can improve its topical relevance and authority together.

Yes, an E-commerce website should create intermediary supporting groups of web pages to consolidate the relevance while making the crawl path of the search engine crawlers more contextual. The referring pages for new crawl queues are prominent to add a proper contextual consolidation. A search engine can have different referring pages for every crawl request, and keeping all the crawl path possibilities contextually relevant is prominent for Semantic SEO. Instead of using big group of product categories, for the queries with different adjectives, adverbs or phrase patterns, intermediary groups can be created.

Having one main topic for a website is not an obstacle to create further sub-topical maps. Keeping every sub-section more relevant is useful for Semantic SEO.

Yes, you should create different article structures for different web page categories. A product page has a main entity and macro context, a product category web page has multiple entities, and their grouper (hypernym) is the main entity which is also an entity type. The macro context of the product category web page for an e-commerce website is the comparing products for the different product dimensions and features. Thus, two different web pages have two different web page layout, and their article structures are different.

For certain types of queries, the product and the product category web pages can be united, because they do not have enough level of search demand. Thus, improving the functionality of a web page by creating a multi-layered web page layout flow is useful for semantic SEO.

Yes, web page design and layout affect semantic SEO Performance. A web page layout and flow of the web page components signal different meanings. The meaning of the text is not conveyed only by the text's itself, but also text's position, shape and mix with others are effective. In the Candidate Answer Passage Design, the Google has modules to see the "aligment" of a text to another one. Or, visual gaps between the text spans can differentiate a text to another for a different context. VIPs is another algorithm to see the contextual connections between different DOM Elements. A search engine can assign different weights to different answer terms based on the DOM Tree, or the style of the text.

Yes, Semantic HTML Affect the Semantic SEO. Using the meaningful HTML Tags in a correct way affect the Semantic SEO Performance. Thus, using the Semantic HTML tags can signal the place of the main content of the web page, or its purpose, role and relevance to the specific queries, and query search session activities. A search engine can parse the HTML better thanks to Semantic HTML Tags during the HTML Normalization.

HTML Normalization is the process of cleaning the fluff of the Document Object Model within an HTML Document to extract only the relevant HTML Elements for Information Retrieval processes. The main purpose of HTML Normalization is to keep the relevant parts closer to the evaluation process of a search engine. The evaluation and association processes come before the Ranking of a Document. Understanding Initial and Re-ranking algorithms are important because of this reason.

The HTML Normalization is harder if the DOM tree is complicated and hard to parse. If a document is hard to parse, normalize, and evaluate, its semantic relevance, and consolidation are lowered. In the Semantic SEO Course, the process of the Semantic SEO are explained along with HTML Tags and HTML Normalization with Semantics.

To define what every content item should include, the word centrality, or the words of interest are picked. A word of interest represent a word that is the most prominent for the borders of the specific meaning. To define the phrases, or N-grams that exist for every web page, and content item, the Source Context is used. The side-wide N-grams represent the context of the source. The most prominent concepts should exist within the main content of the documents site-wide.

Topical Maps are prioritized or delayed based on their prominence to the query clusters. A prominent query cluster signals the core section of a Topical Map. The corse section of the topical map is connected to the source context directly. The core section of the topical map is completed first, and prioritized for the publication. The historical data changes based on the publication of the documents.

A website publishes 300 content items for a specific topic can be seen as relevant to that specific topic. But, if the main topic of the source is something else, the historical data can make the change of contextual relevance harder for the search engine's decision trees. Thus, prioritizing the core section of the topical map is necessity.

The contextual relevance, diversity and themes of words are processed in the Semantic SEO Course.

Topic Modeling and Semantic SEO are connected to each other to provide a better contextual consolidation. Every website has a different topic model. The Topical Distance is measured with topical relevance and the Categorical Term Frequency and Inverse Document Frequency.

Python can be used for Topic Modeling with BERTopic or Gensim. But, using Python can make a Semantic SEO slower, or being mistaken. Using the human senses and thinking power is always better for Semantic SEO. Machines can be used for scaling things up faster via Semantic SEO.

In Semantic SEO, The Keyword Cannibalization is defined based on the conflict of the Search Intent and the Query Definitions.

A query can have different definitions. The Query Processing and the Query Rewriting methodologies along with the Query Types such as Synthetic Queries are a must for understanding Semantic SEO. A Query Cannibalization happens between the same search intent and same query definition in Semantic SEO.

A web page that targets Query "A" and another web page targets Query "A" from the same website is not enough for creating cannibalization. Because the first page can target the "Buy A" query definition while the other one targets the "What is A" query definition.

Thus, using internal links, proper web page layouts, and web page introductions are prominent to avoid query cannibalization. In Semantic SEO, keyword cannibalization happens for the same search intent with the same main entity.

A website with high authority can rank both of those web pages with a nested SERP Snippet. A lesser authoritative source can rank only one of those web pages while ranking the other one for multi-word queries.

To Clean, a Blog with Thousands of Web Pages, the Semantic Distinctiveness and Semantic Relevance are used. The underperforming web pages can be deleted, but this situation can affect the Document and URL IDs. URL IDs and Document IDs represent the specific web page and its content within the indices of the search engines. The authoritative URLs can be affected by deleting the existing web pages since these are connected to each other, and they are neighborhood content of each other.

• To fix the existing website based on the Semantic SEO, the closely related web pages are gathered into similar web pages.
• The internal links with the same anchor texts to the different web pages are cleaned and fixed.
• The contextual vector broken points are cleaned and some content sub-items are moved between the web pages.
• The URL Structure and the Content Network changes should be done at different times.
• The real-world search data is used to create a better contextual consolidation.

The Semantic SEO Course processes the optimization of the existing websites with Semantic SEO.

Yes, Semantic SEO is for every type of website. Semantic Search Engines rank websites based on the semantics of users, queries, words, and entities. Thus, semantic SEO is a practice for every website type such as an e-commerce or a service website.

But from an E-commerce website to the service website the Semantic SEO practices change. An e-commerce website focuses on the products, and product usage, payments, along with product comparison while a service website focuses on the local entities, or service-related existing tools. One of them focuses on "how to" type of content while other one focuses on "buy" type of queries.

Semantic SEO interprets these differences for creating a better Semantic Content Network. But, generating the new and unique questions with different Language Models is necessity. The Language Models are processed in the fourth lecture of the Semantic SEO Course.

Yes, Structured Data is useful for Semantic SEO. The structured data usage supports the context of the specific document along with its purpose. A document with "How to" root, and "How to" structured data align with each other. Consistency is the favorite meal of semantic search engines. Doing a thing for a web page might not change the situation while doing SEO optimization for 100 pages create feedback from the search engines.

The Complex Adaptive Systems and Consistent Minor Changes represent the quality understanding of the search engines.

Using multiple Structured Data samples without a proper hierarchy, and connection can be seen as a spam activity by the search engines. Thus, using the structured data if only it is necessary and consistent with the content on the web page is useful for Semantic SEO.

The Complex Adaptive Systems and the Quality Assignments are processed in the Semantic SEO Course.

The first thing to do Semantic SEO for a brand new website is creating a Topical Map along with aligning the URL Structure and designing the Homepage layers. The homepage of a website defines the specific website's context, and identity. Thus, it is prominent.

Article length is not a ranking factor, or relevant to the Semantic SEO. The meaning in the article and the word connections are relevant to the Semantic SEO. Since the root pages and seed pages have better coverage than the singular content items, they are usually longer. Since article length and information amount in an article are correlated with each other, it is normal to have longer articles.

To create an information gap between the competitors, it can affect the article length. But, solely the character count, or word count are not relevant to the Semantic SEO, or ranking of the documents. Unique sentence count, and unique word count can be a signal for expertise, but still, it is not relevant to the length of the article.

No, Semantic SEO doesn't prevent web page design or On-Page SEO practices. Semantic SEO needs to be integrated with the design of the web page, and On-Page SEO practices. Semantic SEO can change the color palette, order of web page layout components, or web page design entirely. A text with a larger font size can pass more relevance than a smaller font-size text. Thus, Semantic SEO is at the center of Web Page Design and On-Page SEO.

Yes, Semantic SEO is relevant to the Web Page Loading Time and Performance (Page Speed). A slow web page can be relevant, and authoritative, but if it doesn't provide quick initial contact with the user, the possibility of click satisfaction is lowered. A lower click satisfaction possibility lowers the chance of Semantic SEO for succeeding a better ranking. A fast web page can provide a better initial contact, but it can break the web page layout and alignment of elements with each other. Thus, the broken layout can affect the connections of the text spans. With all these, the Largest Contentful Paint Element is seen as the purpose of the web page. Changing the LCP Element can affect the meaning of other connected text spans. In this context, a proper and consistent web page loading performance and the process are necessary for Semantic SEO.

To use the Semantic SEO for a YMYL Niche, the content structures, and contextual consolidation is deepened. Some of the necessities for Semantic SEO in the context of Your Money or Your Life websites are below.

• Including more entities for the specific health conditions
• Including more attributes for the specific health conditions
• Using unique N-Grams for the specific sub-topics
• Using more granular topical maps than the competitors
• Using fewer internal links between the content items
• Improving the Contextual Flow with Structured Language Model
• Connecting more Interest Areas for the specific health conditions
• Defining more concepts than the competitors
• Using shorter sentences than the competitors
• Keeping the publication frequency higher than the competitors
• Updating the existing content pieces more frequently than the competitors
• Using more numeric values
• Using more "Enumarating References"
• Using Preferential Ordeals
• Using the Knowledge Domain Terms to Explain the Specific Topics
• Using the Zero Volume Phrases
• Including the PDF, News and Scholar Sections to the Universal Search Documents
• Using the Social Proof and Active Commenting with Relevant N-Grams
• Including the Official Organizations in the Source Identification
• Using completely original images and visuals to explain the specific problems

YMYL Websites can focus on health niche, or finance and insurance. In every case, the algorithms are more careful to choose the new sources to be ranked. Thus, the declaration structures, order of the declarations, and their discourse integration, the authors and reviewers, their real-world existence are prominent for the search engine. In terms of Semantic SEO, the authors, books, podcasts, or any kind of scientific publication can be used within the articles to provide a Universal Search Perspective in a better way.

To scale the Semantic SEO for millions of articles, the Algorithmic Authorship and the Natural Language Optimization along with the Generation is used. A better document template can dominate the existing sources with high historical data.

There is no need of a tool for Semantic SEO, or scaling the Semantic SEO. But, Python Programming Language and its libraries such as PyTorch can be used for automation at certain point.

No, using a flat URL structure is not better for Semantic SEO. Using a flat URL structure doesn't signal the context of the specific document with a proper folder tree. The Hierarchial URL Structure creates a shorter URL possibility with fewer words within the URLs. Using a deep URL Structure is not a problem for the crawl demand, or the signaling the prominence of a web page as long as there is a proper internal link tree.

Yes, it matters for Semantic SEO. But, it doesn't matter for the crawl rate after 2019. In 2019, it is explored that the search engine doesn't crawl regularly the web pages that are really deep in the click depth. But, after 2019, even if there is no proper URL Structure, or internal link structure, the Query Demand, and Query-Document Relevance create the crawl request quota.

In other words, even if a web page is in deep for click depth, as long as it is supported by the neighborhood content, and an authoritative source, it is crawled and ranked properly. To support these better, a rotating link structure can be used within the Footer, Header, and Homepage. Or, Dynamic Footer Design for different website sections is used.

The Internal Link Structures are explained within the Semantic SEO Course deeply.

Main Elements of Semantic Web are the behavior patterns of the web users. Semantic Web is designed and announced for making machines to parse and understand the data related to real world, such as the working hours of your dentist, or understanding when your doctor is on his/her office. Thus, Semantic Web, and Semantic HTML along with the Semantic Search Engine and Semantic SEO are connected to each other. Main Elements of Semantic Web are semantic HTML Documents, Semantic HTML Tags, and Linguistics along with the NLP.

Yes, Site-tree (Information Tree) matters for Semantic SEO. Having a proper information tree involves a proper deep hierarchial URL Structure along with a proper internal linking structure and navigation for the users. Protecting the contextual convenience between different website segments and sections is to provide a better information tree. An information tree optimization relates the documents and the central words for them better than a flat website structure.

Keeping similar products together within the webserver even helps for faster crawling rates with higher understandability.

Yes, Semantic SEO and Sitemap XML Files are relevant to each other. Because, a sitemap represent the website URLs that a website wants to index. In this context, the most prominent and relevant URLs appear in the sitemap. Having a proper initial indexing, and ranking set up with a proper sitemap and robots.txt file organization is necessary.

Using different sitemap XML files for different website segments can help a search engine to find the relevant URLs to each other faster, and it can download the most prominent URLs more frequently. In the News SEO industry, using smaller sitemaps help for better and faster indexation.

Using different website sitemap XML files is helpful for a better coverage analysis.

Yes, the Robots.txt file is relevant to Semantic HTML. A broken robots.txt file can cause a search engine to ignore the prominent content pieces from a website. A robots.txt file can affect the indexation, crawling, or crawl rate and sitemap location for certain search engine algorithms. Thus, the Robots.txt file can help a Semantic SEO to optimize microseconds.

If a website webserver answers unnecessary crawlers or requests from different scrapers, it can cause Googlebot, Bingbot, and other search engine crawlers to make fewer requests. To prevent such a situation, even if it is for microseconds, using a Robots.txt file for blocking the unnecessary computation is necessary.

Note: Using a proper Robots.txt is effective for having a carbon-free website. Carbon emissions can be in the search engines quality assignments in the future.

Semantic SEO and Topical Authority are defined Koray Tugberk GUBUR as decreasing the cost of retrieval with higher accuracy, and informativeness for a longer time for search engine algorithms, so that search engines can change their ranking preferences without exegenous ranking factors such as PageRank. Thus, Semantic SEO is the strongest way of outranking established authorities for low budget but highly expert, and passionate knowledge domain professionals.

Semantic SEO helps for Local SEO by providing a better local penetration. Google is a hybrid search engine. In other words, different SERP Verticals support each other. Image search results can come from Web Search Results, and Website content can affect the Local SEO Performance. In this context, having a Semantically Optimized Content Network is necessary for a better local search performance.

Besides the searches on the Google Map, or Google Local Map Pack on the Universal Search Results, having the locales and local entities within the textual and visual content of the web entity is useful to provide a better brand penetration for different regions. Using locally integrated and semantically created content networks can create better contextual relevance for certain products, users, or queries for certain geographies.

Generating specific questions, or including specific entities with proper attributes and connections provide the local penetration for the semantic search engines. In the Semantic SEO Course, the process of the Semantic SEO Optimization with Local Penetration is explained.

To learn Python for Semantic SEO, the Python Courses and Python coding practices are necessary. Learning Python is not a must for Semantic SEO. But, Data Science and Data Manipulation are musts. A python is a tool for processing, gathering, aggregating, and visualization of data. A good semantic SEO needs fewer data since the one has enough experience with the search engine.

• A semantic SEO can predict the query volume, and query variations for a phrase.
• A semantic SEO can guess the internal links, and link texts of the best ranking sources.
• A semantic SEO can predict the topical maps of the competitors.
• A semantic SEO can understand the relevant entities to each other by checking only a few web pages.
• A semantic SEO can predict the SERP Features for a query.
• A semantic SEO can understand which questions should be used, and how they should be formulated from the queries.
• A semantic SEO can have empathy with a semantic search engine with the connections of meanings to each other.

Thus, without even data, only with the instincts, a person with semantic SEO capacity, and linguistic background can understand the semantic search engines with insights.

The ideal site structure involves the contextual connections, and mutual attributes between the entities by protecting the source context via the site-wide N-grams and anchor texts. The ideal site structure has a dynamic footer, and header links that change based on the website segment.

An ideal site structure for semantic SEO provides a better contextual flow. It provides a sequential query session within the website by removing the need of a second search session on the search engine's search bar.

Yes, you can rank websites with Lower Article Count by providing a small Semantic Content Network. But, to dominate the niche, a website needs to have "Broad Appeal". Broad Appeal is a concept for the Quality Assignment of the search engines.

Thus, covering side topics, and having a side-section of the topical map with other interest areas help search engines to rank new sources better and faster.

Having a semantic search engine optimization project needs to understand the need for historical data. Via the historical data of a web search engine, the side-topics and the broad appeal can help to increase the confidence score faster.

To ensure better rankings with fewer articles, the article sentences are structured better with a deeper level of information than the competitors.

The Structured Language Model, count of the propositions, declarations, facts, triples and their understandability, sentimental connections, and the overall contextual vector have to be measured for better rankings with small websites.

To use Semantic SEO with a lower budget, the "Argument Filtering" has to be done. Two documents have similar entities and similar facts within it. A search engine needs to differentiate the similar documents from each other. And, if a search engine can't differentiate the documents from each other, they can choose one of the documents as a representative of another one. Most of the time, the representative sources are chosen based on the PageRank and Brand Reputation. Thus, to provide a source from the lackness of branding, Knowledge-Based Trust is used.

Knowledge-Based Trust is explained within the "Creating Semantic Content Networks" SEO Case Study. Knowledge-Based Trust represents the value of the truths within website content. KBT can provide a better authority while normalizing the PageRank difference between the highly linked sources and not linked sources.

Thus, to protect a website with Semantic SEO with a lower budget, the methods below are used.

• Filtering the sentence structures.
• Creating shorter document templates.
• Not focusing on the side section of the topical map.
• Using nested propositions with fewer sentences.
• Using Structured Language Models with tables, lists, or comparison paragraphs.
• Using third-party references from the authoritative social posting platforms.
• Using highly optimized unique visuals.
• Using the different forms of content such as video, audio, images along with the textual data.
• Filtering the entities, attributes, and relevant facts for them.

By writing shorter, giving more facts and information makes content more valuable for a search engine, and users while decreasing the cost of retrieval and increasing the clarity of the content.

Semantic SEO requires expert authors for factual writing. Opinions or text without information can't be used in the articles. Thus, if an author is not expert for a specific topic, the author needs to know how to research a topic. Usually, these types of research needs to create an imitation between the newly created articles and the existing articles within the SERP. A search engine can recognize these types of light paraphrasing and canonicalization between documents can happen on a certain level.

Source Canonicalization and Clustering are processed within the Semantic SEO Course. A canonicalized website can be affected by the Google search engine updates, according to the cluster representative. And, to provide unique content pieces, a Semantic SEO has to change the sentence structures and content brief structure properly. 

In this context, a semantic SEO needs to teach the author how to use the sentences, words, and word connections along with the formatting of the article. An author with research skills can be trained for topics that require an extensive amount of information. Using the same author for the same topic can increase the speed of writing. When choosing freelance writers, a research skillset has to be a requirement.

Content editors and managers can fix the information gaps and the format errors within the articles. In Semantic SEO Projects using authors and editors for different tasks improve the cost of production, and speed.

To call a topic covered completely, the entities, attributes, and their connections to the other entities are processed in a more detailed comparison and consolidated way than the competitors. Processing the entities for different contexts and relational understanding requires making every declaration and statement more detailed, diverse, and repeated with different angles along with formats. A product's size is given in a list, table, or after a comparison heading as a paragraph. A product's size is given for being nth biggest or nth smallest in its own category. A product's size is explained for its efficiency, weight, design, or its usage purposes. A product's size and its effect on the pricing, or the maintenance are explained. A product's size is given with different size measurements. For a single entity, and its single attribute, there are many nested, direct, or indirect propositions with different formats. Once, the process is completed for every entity and its attributes along with the connections. It is assumed that the topical map is completed. But, most of the time, Semantic SEO doesn't need to process everything. Processing only the specific entity as better than the competitors in a specific way To look for ancillary topics, the Semantic SEO audits the competitors' sitemaps and how they structure their content network to take the support of contextual consolidation from the similar entities, and query templates.