What is semantic SEO?
Semantic SEO is the process of creating a content network in a relevant and meaningful structure for each entity within a subject. It involves connecting terms, entities, facts to each other with factual accuracy and relational relevance. By focusing on meanings and topics instead of words, Semantic SEO aims to better satisfy the search intent of the user and establish authority for the Search Engine and the User on a particular subject. Google, as a Semantic Search Engine, creates connections between entities and search intents, organizing the information on the web. Therefore, creating an already organized content structure with clearly connected entities is important for Semantic Search Engine and Semantic SEO.
What is an entity?
An entity, in the context of search engines and natural language processing, refers to a thing or concept that is singular, unique, well-defined and distinguishable. For instance, in the provided information, “Roald Dahl” and “Apple” are mentioned as entities. Entities can be a person, place, object, or even an idea. They play a crucial role in understanding and processing natural language data, and in generating more relevant and logical content for Search Engine Results Pages (SERP).
What is a Triple?
A Triple consists of an entity, predicate, and object. It is a concept used in natural language processing and by search engines for organizing information on the web. For instance, Google mentions using triples for indexing entities and re-organizing web documents. An example of a triple could be “Tom Hanks played” or “Tom Hanks said” types of queries. Another type of triple in semantic SEO is “Entity-Attribute-Value” (EAV). The Entity, Attribute, Value (EAV) Architecture for SEO involves different types of entities, their attributes, and values within the data model to encode a space-efficient knowledge base. This model aims to provide a better representation of knowledge.
What is Topical Authority?
Topical Authority is a concept from Google Search Patents and Research Papers. It is a way of balancing the PageRank for finding more authoritative sources with the information on the sources. In the context of SEO, it is a methodology to rank higher on search engine result pages by processing connected topics and entailed search queries with accurate, unique, and expert information. The concept of Topical Authority was founded by Koray Tugberk GUBUR on 18 May 2022 for conceptualizing semantic content marketing.
What is Query Network?
A Query Network represents a search language with all the possible word distributions in a language by representing the terms in the queries, with different aspects, definitions, and themes. The Query Aspect involves an angle for the search term with certain context signifiers, such as “Hawaii Hotels”, the Hotels represent the “aspect of holiday”, rather than the “construction, or architecture”, while the query definition is the phrasified version of the query terms such as “Reserve a room in Hawaii Hotel”, and while the “Query Theme” represent the direction of the “query aspect”, and it should be matched with the web document’s theme.
What is semantic content network?
A Semantic Content Network is a network of interconnected content where each piece of content is related to others in a meaningful and relevant way. This network is organized around specific entities and topics, with a focus on providing accurate and relationally relevant information. The purpose of a Semantic Content Network is to better satisfy user search intent and to establish authority on a particular subject for both the user and the search engine. This is a key aspect of Semantic SEO, which is about creating content that is not just keyword-focused, but also meaning and topic-focused.
What is Microsemantics?
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. It’s about the finer details in a piece of content that contribute to its overall meaning and relevance in a given context.
What is Macrosemantics?
Macro Semantics is the semantics for the main and prominent parts of the web documents’ relevance and context. This can include elements such as headings, anchor texts, and site-wide n-grams. It’s about understanding the overall characteristics of a content network from a semantic point of view. For instance, it considers the most used nouns, adjectives, or predicates site-wide, the most commonly used question formats, the types of queries targeted, and the construction of heading vectors, among other things.
What is Topical Coverage?
Topical coverage is a metric about the level and competence of a website on a topic. If a source does not have enough content about a topic, its topical coverage will be below. The way topical borders and topics are linked together is critical to Semantic SEO.
What is Historical Data for SEO?
Historical Data for SEO refers to the accumulated data about a source’s quality, reliability, and relevance for a particular topic or user segment. It’s not just about the time a website has been active or its ranking history, but more about the quality and extent of user engagement with the site. This can include aspects like mouse-overs, impressions, and even ranking in the 94th position. This data can signal to search engines the quality of the source, the factuality of its content, and the expertise of its content network. Non-quality user engagement or query session logs can negatively impact a website’s ranking over time. Therefore, it’s important to maintain good historical data with strong signals to improve and maintain a website’s SEO ranking.
What is Relevance for Information Retrieval?
Relevance for Information Retrieval is the 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 the overall connection between the query and the information available. However, it is still considered the Blind Librarian state of the search engine as it primarily focuses on the connection rather than the direct answer to the query.
What are Represented and Representative Queries?
Represented and Representative Queries are concepts from Query Processing. A search term has both representative and represented query versions for expressing itself. These are not the same as “seed query” or “tail queries”. 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.
Representative Queries are broad
Represented Queries are specific
Example:
Represented Queries >> Healthy dinner recipes for weight loss
Representative queries >> Healthy diet recipes
What is Semantic Distance?
Semantic distance refers to the distance between two concepts or existing things with meaning. It is measured using two main methods. The first one involves calculating the association and connection angles and their counts between two entities. The second method counts the length of the connection between two things with certain associations. For instance, if A is connected to B, B to C, and C to Z, the semantic distance between these entities is calculated. The relevance calculation changes how query semantics and document statistics are combined to calculate the semantic distance. Factors such as the documents’ PageRank, their vocabulary differences, and query metrics can influence the semantic distance.
What is Semantic Similarity?
Semantic Similarity refers to the closeness and relevance between two words. It involves the use of semantic relations between words, which is known as lexical semantics, and the distance between words’ meanings, referred to as semantic closeness.
What is Semantic Relevance?
Semantic Relevance refers to the degree to which a certain term or concept is related to the given context or topic. It’s about how closely the meanings of the two entities (terms or concepts) align with each other in a specific context. This concept is crucial in understanding and optimizing the content of web documents for search engine queries.
What is Natural Language Processing?
Natural Language Processing (NLP) is a fundamental Artificial Intelligence (AI) subset that allows computers to have meaningful discourse with humans using natural language. This brings us closer to the aspiration of actual human-machine correspondence. NLP employs Machine Learning (ML), computational linguistics, and statistical analysis techniques. Python plays a significant role in NLP development.
What is Sliding-window in NLP?
Sliding-window in Natural Language Processing (NLP) is a concept to explain the width of the tokenized and processed text. According to the selected tokens, the NLP and Natural Language Understanding (NLU) results will change. Sliding window is used by neural networks and Large Language Models to predict the next word, next sentence, and syntactic and semantic meaning of the words. For instance, the interpretation of the sentence “Koray bought stocks from NVIDIA by trusting the cost of LLM training.” can vary based on the sliding-window width. If your sliding window takes only the first 3 words, the first iteration only approves the “Koray bought stocks”, the next iteration gets the beginning of the methodology, and a named entity, which is NVIDIA.
What is Sequence Modeling in NLP?
Sequence modeling is a term from Natural Language Processing, that involves changing the word sequences for higher responsiveness and contextualization. It’s a type of statistical modeling for predicting the next word in the sequence based on the words that precede it. For example the “Teacher yelled students”, and “Students are yelled by Teacher” word sequences do not distribute the relevance in the same way. This is important for understanding the context and meaning of sentences, especially in machine learning and AI applications.
What is a Central Entity for Semantic SEO?
A Central Entity in Semantic SEO is the main topic or subject that appears consistently throughout every subsection of the semantic content network, whether in the main content (macro context) or supplementary content (micro context). This entity imparts its main and minor attributes to the core and outer sections of the topical map. It always appears inside the anchor texts with a synonym value. The Central Entity and Source Context are united to create a major focus on website content, aiding in creating connections to users’ possible and related search activities. This entity is crucial in determining the direction of the topical map for proper classification with authoritative sources.
What is source context?
Source Context refers to the purpose of the source, such as a website or a web entity like a CEO or Social Media Platforms, and other aspects of a brand. It involves understanding how the brand monetizes its content and how it converts search engine users into customers and clients. The Source Context needs to be connected to the Central Entity with an appropriate attribute. For instance, if the source context is “Visa Consultancy”, it should be connected to relevant aspects of a “country”, like its culture, religion, geography, or climate. The source’s context should be reflected on every web page of the website, in the boilerplate, and in the main content.
What is central search intent?
Central Search Intent is the primary goal or aim that a user has when typing a query into a search engine. It is the intent that will appear in all the topical map and semantic content networks, whether it is in the boilerplate or main content. The central search intent is the unification of the source context with the central entity. It is heavily processed in the core section of the topical map and is reflected on the outer section of the topical map. This intent guides the direction of the content and helps in classifying it properly with authoritative sources.
What is a Knowledge Domain?
A Knowledge Domain refers to the specific areas of queries, entities, layout designs, search patterns, and user segments. It contains specific information, layout design, sentence-information structure, and a user-satisfaction model. For example, in the Currency Knowledge Domain, users typically look at the current exchange rate and then leave the site, meaning a high bounce rate can be a good thing and a fancy layout may not be necessary.
In a Knowledge Domain, search engines like Google might classify content publishers and service providers based on their specialty, authority, and coverage, along with their sum of historical data. They can unify different information sources, such as Gmail, Chrome, or Social Media Activity, to understand the situation of the Source for the specific Knowledge Domain.
A Knowledge Domain can include “experts”, “apprentices”, and “laypersons” based on the Information and Service Sources’ expertise and organization level.
What is Contextual Domain?
A Contextual Domain refers to the specific area of a query where the search intent, user behavior, and content structure are defined according to the specific context. It can be understood as the “context” within which a specific query or search term is being used. The Contextual Domain can impact how search engines like Google interpret and rank content, and it can also influence user behavior and content interaction patterns. It’s important for content creators and SEO professionals to understand their Contextual Domain to optimize their content effectively.
What is Contextual Layer?
A contextual layer represents a specific level of depth and detail within a “contextual domain.” A contextual domain is a broader area of interest or concept, while a contextual layer delves deeper into the details and sub-parts of that domain. It is characterized by the processing angles and context qualifiers used when dealing with a concept or topic. Contextual layers help organize and categorize information based on the specificity of queries and the level of detail required to address them. In the context of Natural Language Processing and Natural Language Understanding, contextual layers play a crucial role in understanding and processing user queries and documents, enabling search engines to provide relevant and contextually accurate information.
Example of Knowledge Domain, Contextual Domain and Contextual Layer.
A contextual coverage can be understood by the “context qualifiers”. A context qualifier can be an adjective, adverbial or any other preposition such as phrases beginning with “for, in, at, during, while”.
The entity-related questions below are not the same in terms of the contextual domain:
- What are the most useful fruits for children with insomnia?
- What are the most useful fruits for children with anxiety?
The entity-related questions below are not the same in terms of the contextual layer:
- What are the most useful fruits for children with severe insomnia over 6 years old?
- What are the most useful fruits for children with low-level anxiety under 6 years old?
The entity-related questions below are not the same in terms of knowledge domains:
- What are the most useful books for children with severe insomnia over 6 years old?
- What are the most useful games for children with low-level anxiety under 6 years old?
But all of these questions can be in the same Semantic Content Network because they are all about the same “concept”, and “interest area” with similar search activity, and search-related real-world activity.
A search engine divides the web into different knowledge domains, and calculates the macro and micro context scores for a source, a web page, and a web page section at the same time.”
What is Lexical Semantics?
Lexical Semantics, also known as Lexicosemantics, is the study of the relationship of words to each other. It includes different types of word relations such as meronyms, holonyms, antonyms, synonyms, hypernyms, and hyponyms. Semantic Similarity refers to the closeness and relevance between two words. The relations between words (lexical semantics) and the distance between words’ meanings (semantic closeness) are used in semantic studies.
What is Query Semantics?
Query Semantics is the study of the meaning of search queries. It includes understanding the intent behind the queries, the entities mentioned in the queries, and the relationships between these entities. It helps in delivering the most accurate and relevant search results to the user. The Semantic Search Engine uses methods like Entity-seeking Queries, Canonical Queries, and Query Rewriting to understand Query Semantics and provide comprehensive results.
What is Contextual Vector?
A Contextual Vector, as described in the document, is a vocabulary list created with a macro-context for each unique term from a domain, based on the number of occurrences of these terms. This concept is part of Google’s User-context-based Search Engine Patent. It helps Google understand the unique aspects of a context within a certain domain, allowing it to differentiate between different user behaviors, expectations, and quality parameters.
What is Content Configuration?
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. This process involves 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 a topical authority, it means that configuration will be needed from the competitor side, mostly.
What is Semantic Content Brief?
A semantic content brief is a document or plan that contains comprehensive information and instructions for the creation of a Semantic Content Network. This brief includes essential details related to lexical semantics, the relationships between entities, and relevant phrases. It may involve utilizing various approaches simultaneously, such as phrase-based indexing and word vectors or context vectors, to assess the contextual relevance of content within a specific contextual domain. The semantic content brief serves as a guide for individuals or teams involved in building or organizing content within a Semantic Content Network, ensuring a high level of understanding and alignment with semantic search engine principles and goals.
What is Main Content?
Main Content is the primary portion of a web document. It encompasses all the context-terms, topical entries, and main entities within the web document. It provides the principal context-flow and coverage, and also includes a summary of the entire article. However, it does not engage with sub-contexts or minor topics. It processes the macro-context of the document and offers an appropriate connection to query contexts and semantics.
What is Supplementary Content?
Supplementary Content is a part of the web document that deals with the micro-contexts, minor entities, and attributes in a way that is connected to the macro-context and main content. It involves more internal links and creates contextual bridges with links or linkless connections. It is used to provide a better association, and “Neighborhood Content” between different segments of the topical map. While it is always connected to the macro-context of the web page, it processes it with a connection to another macro-context.
What is Information Responsiveness?
information responsiveness is defined as being responsive to all related and possible search activities in every form of the search query with all context interpretations as a web source. As a result, natural language generation with high information responsiveness and true expertise and experience signals generated by expert human effort will reward web sources.
Responsiveness is 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).
Responsiveness is a direct Information Extraction Process, and it requires a direct answer that satisfies the possible or related search activities.
To provide the Responsiveness, the web document has to satisfy all the possible needs behind the query.
What is Vastness-Depth-Momentum?
Vastness-Depth-Momentum is a simplification used to explain everything in a semantic content network, and a topical map for gaining topical authority. It essentially means to go faster, go deeper, and go wider. If one of these elements is missing, you should complete its missing effect by increasing another. For instance, if you can’t create a wide source, you should aim to go even deeper and faster. If you can’t go faster, you should aim to create a much wider and deeper source. This concept can be applied in various ways, such as creating a bigger topical map, or publishing deep, comprehensive, and complete web documents for a topic slowly.
What is Topical Map?
Topical Map is a concept used in the semantic SEO discipline. It is introduced for the first time by the author of this document. The concept of Topical Map is not about “focus on topics, not keywords”, “topics in SEO”, or “content planning”. It is a unique concept and most people who come to one-o-one training understand the essence of Topical Maps after spending 5–6 hours together. The exact definition or explanation of a Topical Map is not given in the provided information.
What is Core Section of a Topical Map?
The Core Section of the Topical Map is a concept that focuses on a specific main attribute of the central entity. This specific attribute comes from the source context. For instance, 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. Similarly, 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 needs to be densified further.
What is Outer Section of a Topical Map?
The Outer Section of the Topical Map is designed to focus on the minor attributes of the entity, not the main attributes. Its purpose is to increase overall topical relevance and contextual consolidation of the web source for the specific entity. It also helps to propagate trust and quality signals to the core section of the topical map through links or linkless connections. For instance, if the central entity is “Visa Consultancy”, the outer section would focus on all other attributes for a country, like “religion”, or “language schools”. Similarly, in the case of “Pension and Retirement Planning”, the outer section would focus on all Financial Aspects and Elderly Life.
What is Attribute Prominence?
Attribute prominence refers to how essential a specific attribute is to the definition of an entity. For example, consider the entity ‘Germany,’ which is a country. In the context of the query ‘German league,’ if we remove ‘league,’ Germany remains a country, indicating that ‘league’ is not a prominent attribute. However, in the case of ‘Germany’s population,’ if we remove the attribute ‘population,’ Germany cannot exist as a country without its people. Thus, in this context, ‘population’ is a prominent attribute.
What is Attribute Popularity?
This concept refers to the frequency and volume of searches or demand related to a specific attribute of an entity. It measures how often people search for information about this attribute online. For example, if an attribute like ‘smartphone camera quality’ receives a high number of searches, it indicates that ‘camera quality’ is a popular attribute of smartphones. In essence, attribute popularity gauges the public interest in specific features or aspects of an entity based on how frequently they are searched for on the internet.
What is Attribute Relevance?
This concept focuses on how relevant a particular attribute is, not in relation to the entity itself, but in relation to the Source Context.
Title
In the context of this information, a title refers to the Meta Title of a webpage. It is an important aspect of SEO (Search Engine Optimization) and is often displayed on search engine results pages. The title is meant to give a brief and accurate summary of the content of the webpage. It’s worth noting that sometimes Google may use H1 Tags instead of the Meta Title if it thinks that the H1 Tag can better define the purpose of the webpage.
Description
In the context of this information, a Description refers to the Meta Description of a webpage. It is a brief summary of the webpage’s content, function, and purpose that appears on the Search Engine Results Page (SERP). The Meta Description can be a small direct ranking factor for SEO and can be rewritten by the algorithm according to the Search Engine’s algorithm and users’ queries. It should not exceed 320 characters to avoid being shortened by Google, which could negatively affect user experience.
Image ALT Tags
The Image ALT Tag, also known as the alt attribute or alternative tag, is used to mark images and graphics within an HTML document. If an image cannot be displayed in a browser, the alt tag is shown instead. This tag is crucial for Search Engine Optimization (SEO) as it helps Google understand what the image is about. The alt tag is stored as an HTML element in the source text for an image file. It should describe the image’s content, purpose on the webpage, and increase the relevance of the webpage for user-intent and search intent. It should also be beneficial for web-accessibility and be created according to visual search on Search Engines, possibly including search terms or keywords.
What is a Root Document?
The Root is a central, independent element (page) in a topical map that does not primarily focus on the outer section of the map. It acts as a hub, linking to the most significant elements within the map, and in turn, these important elements link back to the Root. This interconnection means that the Root is tied to every document related to various attributes of the entity, and each of these documents also connects back to the Root.
In the structure of the Root document, the first heading (H1) holds the utmost importance. It is the most significant heading in the entire topical map. This H1 heading in the Root is directly connected to the Central Search Intent, reflecting the core focus of the topic.
The organization within the Root is hierarchical: the most important attributes are linked at the top, while less important ones are placed towards the bottom. Additionally, the H1 heading of the Root, which is crucial for all related documents, should be linked to these documents. This linkage influences the structure and relevance of other headings (heading vectors) within the Root and across the topical map.
What is a Node Document?
A node is a web document that forms part of a semantic content network. There are two types of nodes: quality nodes and non-quality nodes. A quality node is created with the aim of achieving a high ranking in search results. In contrast, a non-quality node is developed to cover secondary aspects of a knowledge domain and is not intended to rank highly. Essentially, while a quality node focuses on visibility and ranking, a non-quality node serves to provide additional, supportive information in a knowledge domain.
Skip-gram Dominant Words:
Identified through word embedding models like Word2Vec. Represent words that frequently co-occur with many other words in a corpus. Act as anchor points in the embedding space, shaping the relationships between other words. Useful for tasks like word sense disambiguation, document summarization, and topic modeling. Close Proximity Keywords:
Defined by their physical proximity within a text, often appearing close together in sentences or paragraphs. May or may not be semantically related, depending on the context. Useful for tasks like keyword extraction, text summarization, and information retrieval.
🌴 Holonym:
A term that denotes a whole, the part of which is denoted by another term.
Example: “Face” is a holonym of “eyes.”
🌴 Meronym:
It is a part of the holonym, and by combining with other meronyms, it makes up the holonym.
Example: “Nose” is a meronym, and in combination with other meronyms like “eyes”, “lips”, etc., they make up the “face”, which is the holonym.
🌴 Hypernym:
It’s a broad term for a group.
Example: “Color” is a hypernym of “red,” “green,” and “yellow.”
🌴 Hyponym:
A specific term that comes under a general term or a group.
Example: “Red” is a hyponym of “color”.
🌴 Synonym:
Words with different pronunciation or spelling but have the same meaning.
Example: Hard, difficult, challenging and tough are synonyms.
🌴 Antonym:
Words that are related to each other but have opposite meanings.
Example: “Sleep” and “Awake” are related to each other but have opposite meanings.
🌴 Polyseme:
Words that have two or more related meanings.
Example: “Bright” can refer to “shine” and “intelligence” simultaneously.
🌴 Homonym:
Words that have the same spelling and pronunciation but different meanings.
Example: “Bear” can have different meanings based on its usage in a sentence. As a noun, it refers to an animal, but as a verb, it indicates a condition.
💡 Why does it all matter?
Focusing on lexical relations between words is important both at the document level (micro-semantics) and in planning the website architecture and topical map (macro-semantics).
The Benefits?
⛳️ Understanding lexical relations between words helps you better close the gaps in the topical map.
⛳️ Better context consolidation of a page on a single topic.
⛳️ And fewer chances of Information Retrieval score dilution of a page by increasing the relevance and click satisfaction possibility.
Focusing only on keywords to optimize your content is flawed.
Why?
Keywords contain words.
And words relate to each other
But,
Not all the relations can be encompassed by keywords.
To understand the relationships of words you need to go beyond keywords.
That is what lexical semantics is.
In lexical semantics, you focus on how close or distant words are to each other according to their meanings.
Credit ref . – https://bow-dietician-9de.notion.site/Semantic-SEO-Glossary-413a22e562d64e06a7ebb51b7dc3c9cb
