Lesson 55: Turning Queries into Headings
Key Takeaways
- Hello, this is the 51st lecture of the Samantha Casio course.
- In this lecture, we will look at another content brief, and we will try to understand the methodology a little further.
- This time, we have just two columns, and one of them is actually from Medical News Today, the other one is actually from the Healthline.
- One more time, the queries that you have seen in this part, they are not ordered according to the volume, they are ordered according to rankings of the specific URL that appears in this area.
- And we see that this specific web page that we are trying to outrank, it is actually ranking for the answer term 70%, not 69, not 71.
Core Concepts
Main Teaching
Hello, this is the 51st lecture of the Samantha Casio course. In this lecture, we will look at another content brief, and we will try to understand the methodology a little further. This time, we have just two columns, and one of them is actually from Medical News Today, the other one is actually from the Healthline. Again, we are trying to be classified with this website, but since this is one of the most comprehensive web pages according to query profiles on this specific context network, we will basically actually target this specific web page, but we will try to be also related to and clas...
How It Works
One more time, the queries that you have seen in this part, they are not ordered according to the volume, they are ordered according to rankings of the specific URL that appears in this area. And we see that this specific web page that we are trying to outrank, it is actually ranking for the answer term 70%, not 69, not 71. We can talk about the numbers a little later, because the numbers inside the queries, they affect the specific URL that appears in this section. And we see that some search content left few tags behind.
Why This Matters
Some search contents, which typically��요 is a cyber proxy that we have applied to Meth. But we are guessing, they may affect a relevance in a really different way. Sometimes it's not just about the exact number, even if the number is not exactly the same. If you are using a closed rated number like 71, still, you can be relevant.
Implementation Notes
And this is about a little bit truth ranges, but another topic. So basically, once we start to generate questions from queries for these area, we will be creating our contextual vector but we will happen if the questions are already inside the queries. In other words, what will happen if the queries are explicit queries that do make the search purpose look likekoast, especially a site like this. No matter how well you do something such as doing, theOs Durkin is likely to let up, at least, as a matter of are explicit question queries in this case we will need to we will need to deepen the ques...
Koray's Terminology
| Term | Meaning in Context |
|---|---|
| Semantic Content Network | Collection of connected, semantically optimized web documents organized for comprehensive topical coverage |
| Contextual Vector | The ordered sequence of headings and content that creates a straight-line flow of context through a document |
| Contextual Hierarchy | The arrangement of heading levels and content weight that determines which context is most prominent |
| Macro Context | The main topic and primary context of a web page, processed in the main content area |
| Micro Context | Sub-topics and supplementary contexts processed in the supplementary content with internal links |
Practical Application
- Analyze the query network for your target topic using search console data
- Create a content brief using Koray's contextual vector methodology
- 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
- Apply the frameworks discussed to a test website or content network
Connection to Framework
Full Transcript
Hello, this is the 51st lecture of the Samantha Casio course. In this lecture, we will look at another content brief, and we will try to understand the methodology a little further. This time, we have just two columns, and one of them is actually from Medical News Today, the other one is actually from the Healthline. Again, we are trying to be classified with this website, but since this is one of the most comprehensive web pages according to query profiles on this specific context network, we will basically actually target this specific web page, but we will try to be also related to and classify together with this specific section. One more time, the queries that you have seen in this part, they are not ordered according to the volume, they are ordered according to rankings of the specific URL that appears in this area. And we see that this specific web page that we are trying to outrank, it is actually ranking for the answer term 70%, not 69, not 71. We can talk about the numbers a little later, because the numbers inside the queries, they affect the specific URL that appears in this section. And we see that some search content left few tags behind. Some search contents, which typically��요 is a cyber proxy that we have applied to Meth. But we are guessing, they may affect a relevance in a really different way. Sometimes it's not just about the exact number, even if the number is not exactly the same. If you are using a closed rated number like 71, still, you can be relevant. And this is about a little bit truth ranges, but another topic. So basically, once we start to generate questions from queries for these area, we will be creating our contextual vector but we will happen if the questions are already inside the queries. In other words, what will happen if the queries are explicit queries that do make the search purpose look likekoast, especially a site like this. No matter how well you do something such as doing, theOs Durkin is likely to let up, at least, as a matter of are explicit question queries in this case we will need to we will need to deepen the question context and write different variations of the specific questions as well in this case here we ask the question in the best possible format what is the water percentage in body because whenever we use the specific answer terms for this question it will be an is is sentence always is or are it will be there then after this after determining our specific let's say the macro context then we start to go variating the questions further like which organs in human body have higher water percentage because even if your body has a certain amount of water or percentage of water let's say 70 according to the lexico semantics the body has different parts but which part has what percentage of water and here we go to the organs then we also go to the ontology of it like higher or lower and what is the water percentage in female and the male body or the child's body and according to what age and what conditions then how is the water percentage in animal bodies different from the human body because most of these questions yes it always asks the human body human body is always directly inside the queries or it is inside the query by by meaning that you you or we are human but at the same time the alternate context here which is connected back to the actual human body is the animal body so we go to the actual sub parts of body which is human body and we go to the alternates or the siblings of the human body again we are using lexico semantics in this case we ask animal bodies because which animal it is is not certain that's why we are trying to use the most relevant animals to the humans and we try to compare it like is it more is it less then how does water help body functions and as you remember body functions is something that we directly actually mentioned in the introductionary part of benefits benefits of drinking water it is also mentioned in the water intake in this area too how much of your body weight is water which is a different variation of the same question but this time we don't talk about the percentage we talk about the kilogram which or lbs etc however whichever you use and in this case imagine that neural matching or end type matching will be changing because measurement unit that we are using change is not a percentage anymore then we ask does drinking water help for weight loss which is another topic which goes to the benefits of drinking water which is our root of the semantic content network what is the optimum percentage for water in the body and we here we talk about actually the optimum water take and also the benefits that will be coming from that which is that's why we keep it closer to theаковices to give space for more counselling and also to give some steps to the this specific anchor text as you see we have used only a single anchor text and it doesn't appear in our macro context it's coming from the supplementary part of the content and also the micro context and it is coming from a boolean question with an h4 because we want it to be even more specific or the precise or the specific anchor text as well and in the next lecture we will try to examine our contextual hierarchy and the structure for instance here we have some doctor names or some sentence examples and a little bit deeper because this time our contextual vector is shorter but when it comes to the structure it's more deeper and we will look at that