16/02/2024
Hello friends, I hope you are all doing well.
Today I will share with you a simple step on how you can understand the concept of Semantic SEO Topical Map and Lexical Semantic SEO Relationships.
In the last 10 days, I read the case study of 30 websites thrice but still, I had some questions and my mind was confused.
I searched things on Google but still nothing came to my mind and then I went to the same page again and started reading this website Myros.com case study.
However, in the meantime, I found the profile option in the bottom left corner, I clicked on
Koray Tuğberk Gübür profile and then I got a chat bar where I got a lot of standard stuff that helped me understand different aspects and concepts. of Semantic SEO and Topical Map.
After reading this I realized that most people in this community already send me a lot of messages in their inbox and the main question is what is semantics and how can they learn?
"So here's a process"
The following questions in this post are not just questions but complete concepts that you can copy and ask on chaGPT I am just a five-year-old kid. Here are the concepts and definitions. It is very difficult for me to understand,
Can you make them easy for me You can use the same tricks with Bard or any other text predictor tool.
Anyone who needs a Topical map for his website let me know
Questions and answers
What is semantic SEO?
Semantic SEO is a method that involves creating a content strategy for Holistic SEO. It's a thinking system that increases critical and analytical thinking power by showing the interconnection of concepts for Holistic SEO and improves the Search Engine Understanding ability. It aims to answer all the questions that a user may have about a particular subject, rather than just a single question and presents the necessary information in a hierarchical structure. Semantic SEO creates complete, comprehensive, and guideline content which can include various web applications, visual and video content, text content or surveys, and PDF Files that meet the user's needs. It offers an organized user-journey experience, as opposed to randomly generated individual content.
What is Topical Authority?
Topical Authority is a concept from Google Search Patents and Research Papers. It's a semantic SEO methodology to rank higher on search engine result pages by processing connected topics and entailed search queries with accurate, unique, and expert information. This concept is a way of balancing the PageRank for finding more authoritative sources with the information on the sources. It was founded by Koray Tugberk GUBUR on 18 May 2022 to conceptualize semantic content marketing.
What is a 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”. The query definition is the phrasified version of the query terms such as “Reserve a room in Hawaii Hotel”, and the “Query Theme” represents the direction of the “query aspect”, and it should be matched with the web document’s theme.
What is a semantic content network?
A Semantic Content Network refers to a structured and interconnected web of content that is created with a focus on meanings and topics rather than just words. This network connects terms, entities, and facts to each other within a context of factual accuracy and relational relevance. The aim is to satisfy the search intent of the user better and establish authority for the Search Engine and the User on a particular subject. It is a crucial component of Semantic SEO.
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. Macro Semantics involves understanding the overall characteristics of a content network from the point of view of semantics. It looks at aspects like the most used nouns, adjectives, or predicates site-wide, the most commonly used question formats, the types of queries targeted, and how heading vectors are constructed. It also evaluates the first words of the paragraphs to check if they answer the question or use rhetoric without information, among other things.
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 search engine uses triples for indexing entities and re-organizing web documents. An example of a triple could be "Tom Hanks played" or "Tom Hanks said". Another type of triple in semantic SEO is "Entity-Attribute-Value" (EAV), as explained by Koray Tugberk GUBUR in the EAV SEO Case Study.
What is Historical Data for SEO?
Historical Data for SEO refers to the accumulated data regarding a source's quality, reliability, and relevance for a specific topic or user segment. It can be linked to various types of search intents and concepts with a click satisfaction possibility. Historical data is not merely about "time", but also about "user engagement" and the quality of this engagement. It includes aspects like mouse-over, impressions, and even ranking in the 94th position. Non-quality user engagement or query session logs can negatively affect your website after a certain amount of time. If you lose your rankings today, it could be due to the historical data from 6 months ago. To clean the bad historical data for a website, you need good historical data with a stronger signal.
What is Topical Coverage?
Topical coverage is a measure of the extent and proficiency of a website's content on a specific topic. If a website does not have sufficient content about a topic, its topical coverage will be low. Creating a topical border is essential to calculate the topical coverage. This involves the connected attributes, knowledge domains, and contextual domains for a specific query network. It's important to note that topical coverage cannot be increased simply by stuffing entities, attributes, or opening a new page for every topic. The way topics and topical borders are interconnected is crucial to Semantic SEO.
What is Relevance for Information Retrieval?
Relevance, in the context of Information Retrieval, refers to the score that comes from certain text processing methodologies. These methodologies include term saturation, length normalization, co-occurrence matrix construction, BM25, TF-IDF, GlovE, Word2Vec, and more. It essentially showcases the overall connection between the query and the retrieved information. However, it's important to note that despite showing this connection, it is still considered the Blind Librarian state of the search engine. Relevance is primarily concerned with improving the Information Retrieval Score, which comes from Term-weight Calculation, and it involves reading the Retriever's mind with mathematical algorithms.
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 different from what you would call "seed query", or "tail queries". The relevance of the terms put into the search bar is distributed based mainly on the representative queries. For instance, the queries "board vision", and "vision board" can yield different results, even if they mostly mean the same thing. This is due to the fact that there will be different contextual connections and query interpretations for both of them. The representative query has access to the represented query's relevance, indicating the interplay between the two.
What is Semantic Distance?
Semantic distance is the measure of the distance between two concepts or existing things with meaning. There are two main ways to measure this distance. The first is by calculating the association and connection angles and their counts between two entities. The second method is by counting 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, it means that the semantic distance between these entities can be calculated. This calculation can change based on factors such as the documents' PageRank, their vocabulary differences, and query metrics. The semantic distance between attributes and attribute-context pairs can also help search engines rank the attributes based on their importance in defining the entity.
What is Semantic Similarity?
Semantic Similarity refers to the closeness and relevance between two words. It involves considering the semantic relations between words, also known as lexical semantics, and the distance between words' meanings, referred to as semantic closeness.
What is Semantic Relevance?
Semantic Relevance refers to the appropriateness and significance of terms for the given context to reflect the meaning of the term in that context. It's about how closely the meanings of two or more pieces of content match each other. This can be applied to a single word, a sentence, or even a whole document. It's a crucial concept in information retrieval systems, including search engines, as it helps determine which documents are most likely to satisfy the user's needs.
What is Natural Language Processing?
Natural Language Processing (NLP) is a fundamental subset of Artificial Intelligence (AI) 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 a 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 example, the sentence of "Koray bought stocks from NVIDIA by trusting the cost of LLM training." might be interpreted differently according to 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 concept in Natural Language Processing (NLP) that involves changing the sequence of words for higher responsiveness and contextualization. It plays a critical role in understanding the context and meaning of sentences. For example, the sequences "Teacher yelled students" and "Students are yelled by Teacher" do not distribute the relevance in the same way. Sequence modeling helps in optimizing the word-by-word arrangement in documents for improved relevance and responsiveness.
What is the Core Section of a Topical Map?
The Core Section of the Topical Map focuses on a specific main attribute of the central entity. This specific attribute comes from the source context. For instance, if the central entity is "Visa Consultancy", the core section will focus on the "Visa" attribute. Similarly, if the central entity is "Pension and Retirement Planning", the core section will be about "Retirement" under the context of "Financial Independence". The core section is densified further according to the source context.
What is the Outer Section of a Topical Map?
The Outer Section of the Topical Map focuses 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 propagates trust and quality signals to the core section of the topical map with links or linkless connections. For instance, if the central entity is "Visa Consultancy", the outer section will focus on all other attributes for a country, such as "religion", or "language schools".
What is a Central Entity for Semantic SEO?
A Central Entity in the context of Semantic SEO 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. This entity gives 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 together to create a connection and major focus on the website content for creating a connection to the users' possible and related search activities.
