Lesson 5: Introduction to Koraynese (Koray's Framework) (5 Min.)
Key Takeaways
- In my framework, I always create these type of drive folders.
- That's why I would suggest you to check actually on my YouTube channel or all the case studies that I publish for just free and over half a million words and 60 hours of videos.
- And most of the time, the most popular query, sorry, the entity type inside this query network, is always a country.
- That's why most of the topical maps that I see, they are wrong.
- These are the most important notes here because inside the content configuration, we basically increase relevance and query responsiveness.
Core Concepts
Main Teaching
So, in my methodology, welcome to the Semantic SEO course. This will be the first lecture and I will be explaining Cori's framework. In my framework, I always create these type of drive folders. In these drive folders, you can find About Us page briefs, which will be used for defining our source context so that we can connect every entity to ourselves in a better proximity or relevance.
How It Works
And we also design internal links or we do many other things here. But most important two folders here are content briefs and articles. In the lectures, I won't dive into the theories that much. That's why I would suggest you to check actually on my YouTube channel or all the case studies that I publish for just free and over half a million words and 60 hours of videos.
Why This Matters
They actually wait for you with all the concrete examples and the concrete live or living websites and the real world SEO case studies. And if I open this, you will realize that there is already a topical map there. And the topical map actually has three main parts. These are just the continents.
Implementation Notes
North America, Europe, and Asia. The reason that actually we use continents in this part is understanding the query networks. For example, if I use my paint one more time, if these are different types of query networks that are connected to each other, you actually have a task here. And it is find the most popular entity type in the query networks.
Koray's Terminology
| Term | Meaning in Context |
|---|---|
| Topical Map | Content network design based on semantics with core and outer sections, processing a central entity with main and minor attributes |
| Source Context | The purpose of the brand identity and how the brand monetizes its content |
| Content Configuration | The continuous process of updating content based on changing semantic distances and similarities |
| Semantic Distance | The measured gap between two concepts in terms of meaning and query association |
Practical Application
- Create a content brief using Koray's contextual vector methodology
- Audit your internal linking structure against Koray's anchor text principles
- Map out the central entity and source context for your project
- Study the concepts presented in this lesson until they become intuitive
- Review the related case studies mentioned by Koray for real-world application
- Practice identifying the key terminology in your own SEO projects
Connection to Framework
Full Transcript
So, in my methodology, welcome to the Semantic SEO course. This will be the first lecture and I will be explaining Cori's framework. In my framework, I always create these type of drive folders. In these drive folders, you can find About Us page briefs, which will be used for defining our source context so that we can connect every entity to ourselves in a better proximity or relevance. And we also design internal links or we do many other things here. But most important two folders here are content briefs and articles. In the lectures, I won't dive into the theories that much. That's why I would suggest you to check actually on my YouTube channel or all the case studies that I publish for just free and over half a million words and 60 hours of videos. They actually wait for you with all the concrete examples and the concrete live or living websites and the real world SEO case studies. And if I open this, you will realize that there is already a topical map there. And the topical map actually has three main parts. These are just the continents. North America, Europe, and Asia. The reason that actually we use continents in this part is understanding the query networks. For example, if I use my paint one more time, if these are different types of query networks that are connected to each other, you actually have a task here. And it is find the most popular entity type in the query networks. And most of the time, the most popular query, sorry, the entity type inside this query network, is always a country. And how you can actually classify the countries. One method is local proximity. If you get the local proximity between these countries, then you will get the continents. From there, you will actually dive into the lexical relations. In other words, if you are able to rank for European visa, it means that ranking for Germany visa will be very much easier. And this section brings us also to the concept of semantic distance. And I will show you how to do that. And also similarity. Because the Germany, if I will tell you that, what is the closest other most relevant entity to Germany, most people will actually use something that Germany already entails like Berlin. But it's not a country. I need another entity from the same type. So in this case, actually, again, the most relevant country will be one of the actual neighbors. But this is a logic that comes from the dictionaries and semantic SEO. In my methodologies, we don't use encyclopedia. That's why most of the topical maps that I see, they are wrong. They are created as a concept map, not as a topical map. When I look at here too, you have Germany. The closest or the most semantic relevant country to here, it will be coming from actually user behaviors. In the content configuration section, when I open it, you will see that the closest country here is actually Bulgaria or Turkey or Lithuania. I guess I will be writing it right, I hope. Or these type of countries. And there's a certain reason for that as well. So find the most popular entity type. Then create your topical map according to that entity type by taking all the attributes and prioritize your topics and create your topical borders according to the proximity between them. If I open here, if I open this specific file here, Europe, you will see all the countries in Europe are directly here. And if I am able to open here the Germany, you will see that there is a separate sheet. Inside this separate sheet, you will realize that actually there are many things. But the first section that I was showing you is content configuration. So you see here student visa, education visa, languages and the Erasmus and internship. These things are here. These are the most important notes here because inside the content configuration, we basically increase relevance and query responsiveness. By adding new information or removing some sections, changing internal links, we configure the content for higher relevance as always on. To be able to do that, we always check how the article ranks, what are the new queries, lost queries, shared queries. And then according to that, we continue to prioritize certain sections with each other. So here, if I check my notes, I always see that actually Bulgaria and Lithuania, they always appear too. So the thing here is that when you create a specific, entity attribute pair and when you create a web page for it, if that web page ranks well, you should understand that probably it will be happening for another entity from same type. That's why we go to the other countries with the similar attributes. And at the same time, we realize that even if we write for Germany, we take impressions from Bulgaria and Lithuania. It happens because of the Erasmus because all the students from these regions, they search for Erasmus visa. And since people search for Germany, from these other two countries, search engine assumes that these countries are related to each other, even if the local proximity is not that much high. So in this case, it means we need to change our topical map, configure it better, and we need to increase the relevance between these things. So the search engine actually can rank Lithuania related articles for Lithuania related queries and German related articles for German related queries as well. And we will be using internal links between them so that they can support each other. In a better way. To be able to find these type of things, always you will need to check the queries or the search query sessions. In the next lecture, we will talk about that.