Task:
Please review the following video transcript and perform a comprehensive edit to ensure it meets professional standards.

Instructions:

1. Correct Grammar, Punctuation, and Spelling:
- Identify and fix any errors in grammar, punctuation, and spelling throughout the transcript.
- Ensure that sentences are structured correctly and convey the intended meaning clearly.

2. Verify Terminology and Concepts Related to Semantic SEO:
- Check that all technical terms and concepts associated with Semantic SEO are accurately used.
- Replace any incorrect or ambiguous terminology with the appropriate industry-specific language.
- Ensure consistency in the use of terms throughout the transcript.

3. Enhance Clarity and Precision:
- Rephrase any awkward or unclear sentences to improve readability.
- Eliminate unnecessary repetition or redundancy.
- Maintain the original meaning and intent behind each statement.

4. Preserve Original Meaning and Tone:
- Retain the speaker's original tone and style to the extent possible.
- Ensure that the edits do not alter the intended message or nuances conveyed in the transcript.

5. Format for Readability (Optional):
- Organize the content into clear paragraphs.
- Use headings or subheadings if it helps improve the structure.
- Consider adding bullet points for lists or key points to enhance readability.

Objective:
The final edited transcript should be a polished, accurate representation of the original video content, easy to read and understand for individuals interested in Semantic SEO.
"""
Hello Welcome to the semantic SEO course This will be the first lecture and I will be explaining core ice framework In my framework I always create this type of drive folders In this 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 to theories that much That's why I will suggest you to check actually on my YouTube channel or all the cases that is that I published for just free and over half 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 thing 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 uh sorry entity type inside this query networks 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 also similarity Because the Germany if I will tell you that what is the closest other most relevant entity to Germany most people would 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 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 you look at here to 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 wrong right I hope or these type of countries and there is a certain reason for that as well So find the most popular entity type and 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 to Germany you will see that there is a spreadsheet Inside this spreadsheet you will realize that actually there are many things But the first section that I will be 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 uh 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 to So the thing here is that when you create a specific entity attribute page 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 that search engine actually can rank Lithuanian related articles for Lithuanian related related queries and German related articles for German related uh 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 
"""