Easy Explanation of Query Semantics SEO Case Study by Koray Tugberk
Behzad Hussain
Helping Businesses Grow with Semantic SEO ? Topical Authority Consultant ? Koray's Framework Implementer ? Schema Markup Lover
Koray Tu?berk GüBüR is the founder and owner of Holistic SEO & Digital. Koray Tu?berk publishes SEO Case Studies, Research, and Detailed A/B Tests along with their results.
Here, we are going to explain and simplify one of Koray's SEO Case Study to help new learners understand complex concepts.
Case Study Title: Query Semantics SEO Case Study - Convince Search Engine to Change Meaning of a Query - Two Websites
Explanation Mechanism here:
Short Summary Mentioned in YouTube Video Description: In this video, we'll be exploring how to convince a search engine to change the meaning of a query. We'll be looking at two separate cases where the search engine changed the meaning of a query, and how we were able to convince the search engine to change its mind.
Query Semantics SEO Case Study - Easy Explanation
After his signature greeting style “Hello”, Koray started the video with announcement of his First course launch. (It’s again almost the same time, he has also announced the second gate opening for his Updated and Extended version of his Topical Authority Course on 25th May 2024. Mark your calendar if you want to become a part of SEO Revolution).
I will explain and demonstrate two different websites with the semantic SEO and at the same time I will try to explain the concept of the query semantics and a little bit different its difference from the lexical semantics as well.
Then Koray introduced some websites that will be explained to understand the role of Query Semantics for convincing the search engine to change the meaning of queries.
-----
Let’s understand the technical terms mentioned in the above content.
What is a Query?
In its simplest form, a query is a search term that a user inputs into a search engine to find specific information. It can be a single word or a phrase.
What is Query Semantics?
(Before starting, keep in mind that it’s from a search engine’s perspective)
Query Semantics is about understanding the intent behind a user's search query. It involves understanding the context, the grammatical structure, and the meaning of the words in a query. This helps Search Engines to deliver the most accurate and relevant search results to the user.
Query Semantics help search engines to understand the similarities between the words, and how search language differs from the natural language.
For example, if a user searches for "Apple", the semantic search engine needs to understand whether the user is looking for information about the fruit or the tech company.
What is Semantic Search Engine?
A Semantic Search Engine is a type of search engine that considers the semantic meaning relationships of words and concepts in order to provide more accurate and relevant search results.
Semantic Search Engine puts frequently searched or asked questions and queries about a certain concept in a Semantic structure, which is influenced by user behavior. This results in a SERP (Search Engine Results Page) Design where information is more organized, logical, and interconnected.
Semantic Search is not only linked to the meanings of words but also to grammatical rules and the way words characterize each other.
-----
2 Websites Discussed in the Query Semantics SEO Case Study
Video Transcript (Koray's Words):
"This one; Civar.com, is a product search engine in other words it is a little too unique because it is too unique because it is a product search engine but beyond that because it doesn't sell something online it just tells users to search this thing online and then go to a specific store and just try it physically so they try they have to track all of the physical storage or they have to understand the stock information so that they can tell you that yes if you like these shoes you need to go to these store from this city in this area and it will be open on until to the death point so this is this kinds of uh search engine in other words it is really unique search engine so as an SEO it is also a good feeling that you are doing actual search engine optimization for a search engine design which is a unique new search engine type and I like actually doing that and I can show you the results here too once I published actually these to read by telling that there is no discipline in the client because the client didn't even realize that they launched the project without perfect conditions and anyway this was a quick launch a quick increase as you see suddenly and this is the initial coverage report I can publish these things easily because it won't harm the project because most of this data is already outdated and I'm sure that you won't be able to create same type of website and here too these are the updated results nearly updated results essay from the hrefs as well."
Explanation:
The following websites were discussed by Koray in this video:
1. Civar. com
This is a product search engine itself. It’s too unique because it doesn't directly sell anything online. This website just offers users to search for any product of the mentioned categories and brands. The user gets the following information in response:
So, this is really unique search engine. As an SEO it is also a good feeling that you are doing actual search engine optimization for a search engine design which is a unique new search engine type and I like actually doing that.
Then Koray shows some of his Tweets that mention some facts about this project. That tweet included the following information:
Then Koray showed some initial results from Ahrefs you can see in the video (URL mentioned above).?
Video Transcript:
When it comes to Myros. com, this is also important too because this project is a B2B e-commerce but to be able to be successful on the search engine optimization, sometimes, you have to augment the queries and you need to connect them to your own context that's why it is important to understand the query semantics because this project is for gift selling but not for the users or consumers directly.
Explanation:
Koray explained that this is a B2B e-commerce project and for successful search engine optimization for B2B ecommerce websites, you have to augment the queries and you need to connect them to your own context that's why it is important to understand the query semantics.
Further Explanation:
Augmenting Queries: The concept of "augmenting the queries
Connecting Queries to Your Own Context
Importance of Understanding Query Semantics: The successful implementation of these strategies is connected with deep understanding of query semantics. Query semantics involves comprehending not just the literal meaning of the words in the search queries but also grasping the broader context, intentions, and nuances behind these queries. This understanding allows for a more effective mapping of user searches to the content and offerings on the B2B e-commerce site.
It is actually for B2B selling with a really big amount of materials or the product counts, so the problem, here, is that no one searches it properly even if they search it, they just search maybe 56 queries with a certain type of phrases (or) phrase combinations but when it comes to Turkey, since this is a touristic country, we have many cities ministeristic environment and all these gifts actually have touristic team so to be able to connect these cities to the tourism than to the touristic gifts and then to the B2B selling, you need to understand the importance of the B2C there and also importance of the objects that will appear in the in the gift objects as well.
Explanation:
Myros project is for gift selling but not for the direct users or consumers directly.
Myros is actually a B2B selling project with a really big amount of materials or the product counts so the problem is that no one searches it properly even if they search it, they just search maybe 56 queries with a certain type of phrases or phrase combinations.
Koray explained that Turkey is a touristic country and there is a huge demand of B2C gifts. Tourist searches for individual gifts etc. for there needs but there is a little demand of bulk gifts at a time that Myros was offering.
Further Explanation:
Context of Myros's Business: Myros operates a B2B platform with a vast inventory of products. However, the challenge is that the specific bulk gift products they offer are not being searched for frequently or correctly by potential business customers. The search volume for relevant B2B queries is low, and the searches that do occur may not use the expected phrases or combinations that would typically lead to Myros.
Tourism and B2C Opportunities in Turkey: Koray notes that Turkey's status as a touristic destination creates a significant demand for B2C gifts. Tourists often search for individual gifts, a market that sees much higher consumer traffic and search query volume compared to the niche B2B bulk gifts that Myros offers.
Strategic SEO Shift: The strategy suggested involves initially aligning Myros more closely with B2C type queries and markets, even though its primary business is B2B. This approach leverages the higher volume and visibility of B2C searches to boost Myros's overall online presence.
So in this case, you can't directly go there and create a B2B website you need to change or balance things from the search engine point of view, so that they can actually see you as a candidate there.
So in other words, if someone searches for taking a single gift for from, let's say Antalya with a specific type of team with a specific type of object or something it means that actually you have to be connected to that user so that you can also be connected to the users that actually want to buy B2B products or let's say purchase maybe even two millions of entirely related gift products.
Explanation:
So according to Koray, you need to change or balance things from the search engine point of view so that they can actually see you as a candidate there. In simple terms, Myros needs to be first classified with B2C type websites to be considered for B2B queries further on.
In other words, if someone searches for a single gift (B2C), search engine must connect Myros with that query so that when a person wants to buy bulk gifts (as real targeted B2B user), the search engine also connects Myros with that query.
Therefore, before going towards B2B selling, you need to understand the importance of B2C selling.
In this Myros project, you need to change or balance things from the search engine point of view so that they can actually see you as a candidate there for B2C product queries first and then also relate you with B2B product queries.
Further Explanation:
Connecting B2C Visibility to B2B Queries: By establishing a strong online presence in the B2C market, Myros can create a pathway to being recognized by search engines as a relevant candidate for related B2B queries. For example, if a user searches for individual gifts (a B2C query) and finds Myros, this association can help boost Myros's visibility when the same user or another user searches for bulk gifts (a B2B query).
Importance of Understanding Both Markets: Koray emphasizes that before focusing solely on B2B selling, it is crucial to understand and capitalize on B2C selling opportunities. By first targeting B2C product queries, Myros can build a reputation and search engine credibility. This foundational visibility can then be leveraged to enhance their visibility for B2B queries.
SEO Tactics for Market Balance: Changing or balancing things from a search engine's perspective involves using SEO tactics that enhance visibility across both B2C and B2B queries. This might include keyword optimization, content marketing strategies that appeal to both individual and bulk buyers, and structured data that helps search engines understand the range of products offered by Myros.
So, the another problem here is that even if the a person actually is or let's say has B2B business still they don't use actually these type of queries because the consumers and also be to business owners they usually use the same queries and search engine can't always predict who is who or why the person actually searches it that's why they have to look at the user demand and predicted user profiles so in this case of course user clustering and query clustering and matching queries to the users these are also important subjects but we can talk about audience clustering maybe later in this project.
Explanation:
Another problem is that even real B2B users also don’t use queries with specific B2B search intent or proper context. B2B users (Businesses) use the same queries that are used by B2C users (Consumers). In that case, search engine cannot always predict the true search intent and context of the query.
In the above case, a search engine has to look at the user demand and predict user profiles. Here, user clustering, query clustering and matching queries to the users are the important subjects.
Further Explanation:
Lack of Distinct B2B Search Intent in Queries: One of the main problems highlighted is that real B2B users often use search queries that are indistinguishable from those used by B2C users. This similarity makes it difficult for search engines to accurately predict the true intent and context behind a query. For example, a query for "bulk paper supplies" could be made by both a business looking to resupply an office or a consumer planning a large event.
Search Engine Challenges: Given that B2B and B2C queries can be similar or identical, search engines struggle to discern the user’s intent. Without clear indicators of whether a query is B2B or B2C, the search engine may not always deliver the most appropriate search results for the user’s actual needs.
User and Query Clustering: To address this, search engines employ techniques such as user clustering and query clustering. User clustering involves grouping users with similar search behaviors, which can hint at whether they are likely B2B or B2C users based on their search patterns and history. Query clustering groups similar queries together to analyze commonalities and differences in how different types of users interact with these queries.
Matching Queries to User Profiles: By clustering users and queries, search engines aim to match queries more accurately to the user profiles. This enhances the likelihood of presenting search results that are relevant to the user’s specific context, whether it be B2B or B2C.
To be able to rank for B2B queries I had to create actually a direct consumer website first and then I needed to tweak the sale funnels and also some buttons even or pricing methodologies or other things because if you go to the product pages of this website you will realize that you can even order 100 thousands of props or items suddenly or you can just buy one too so it is actually selling to the consumers but they are not the main target there the problem as I say is query semantics because when it comes to B2B gift related searches it's two less the search demand is too less and they are so blurry there is no specific type of Border or search Behavior differences between these two different distinctive audiences so it means that we need uncertain inference and then we will need to cover all these contexts that will come from these queries.
Explanation:
So, finally to rank for B2C queries first in search engines, Koray optimized the Myros website first for direct consumers (B2C queries) and then also twisted sales funnels, CTA buttons, price methodologies or bulk quantity order options. There is no search behavior differences or borders between consumer’s queries (B2C) and business’s queries (B2B) so there are uncertain inferences or ambiguities for search engines so Koray optimized the Myros website for all those contexts that will come from these mixed queries from both user groups.
Further Explanation:
SEO Strategy for Myros: In response to these challenges, Koray describes his approach to optimizing the Myros website to effectively target both B2B and B2C queries. Initially, he focuses on ranking for B2C queries because of their higher volume and clearer intent. This involves tailoring the website’s sales funnels, call-to-action (CTA) buttons, pricing strategies, and options for bulk orders to appeal to direct consumers.
Adapting to Mixed Query Contexts: Recognizing the overlap and ambiguity between B2B and B2C queries, Koray also adapts the Myros website to handle mixed queries. He optimizes the website for all potential contexts that might arise from these queries, ensuring that the site is equipped to serve both individual consumers and business clients effectively.
Then Koray showed website traffic and ranking keywords from Ahrefs and Semrush tools.
Website: ??????????? ????????????? Civar. com
DR: ?????????????????????? ?????????????? 0
Referring Domains: ?????? ? 48
Backlinks: ???????????????????????? 196
Organic Keywords: ???????? 30.5K
Organic Traffic: ??????????????? 27.5K
---
Website: ?????????????????????????? Myros. com
DR: ????????????????????????????????????? 28
Referring Domains: ?? ????? 236
Backlinks: ????????????????????????? 1.8k
Organic Keywords: ?? ?????? 7.8K
Organic Traffic: ??????????????? 12.7K
But I can tell that with these conditions (technical issues on both websites) and without any kinds of Link or page rank I believe these things are creating good results and there was a strong ontology and taxonomy to create a proper website tree.
Explanation:
Koray mentioned that with such technical issues, without any kinds of Link or page rank, he believed those websites were creating good results because there was a strong ontology and taxonomy to create a proper website tree.
Further Explanation:
Koray emphasizes that despite technical issues and a lack of backlinks or PageRank, a website can still achieve good SEO results if it has a strong ontology (clear definition of relationships between concepts) and taxonomy (logical categorization of content). This proper structure (website tree) allows search engines to understand the website's context and content comprehensively, leading to improved rankings.
Ontology: In the context of SEO and web architecture, ontology refers to a structured framework that defines relationships between different concepts or entities. It enables search engines to better understand and classify content.
Taxonomy: A taxonomy is a hierarchical classification of content. For websites, it involves categorizing content logically so that related pieces are grouped together, making it easier for both users and search engines to find and understand.?
Especially when it comes to civar .com, the main problem was actually URL structure because we can't use coordinates in the URLs so that we shall not create millions of endless URLs to the search engine without a meaning. If you do that as Bing says here; “user demand is important thing to understand whether there is actually a kind of spam or not.”
If you create too many pages without any kind of user demand, search engine might assume that it is actually spammy bad page, that can be generated by a machine.
When it comes to Google if there is no user demand or proper user demand, they assume that it is an unpopular topic but still it doesn't mean that it might not be posted as pump 2 so it means actually you are not entirely in a safe Zone.
Explanation:
Koray mentioned that the main issue with civar. com was their url structure. Urls contain coordinates, like “?p=4081061”, at the end of each product page’s url. Following is a sample url:
www.civar. com/ev-mobilya/sofra-mutfak/pisirme/berghoff-leo-slate-aluminyum-tava-28-cm-?p=4081061
Koray said these coordinates’ addition can’t be used and creating millions of pages without any meaning and benefit to the users may be considered as spam by search engines.
Then Koray shared a blog post from Microsoft blog: How Bing Delivers Search Results
Following is that abstract from Microsoft Post that Koray showed to explain that Search Engines may consider pages with no user demand as Spam:
“Content Moderation Based on Quality, Safety and User Demand
In certain circumstances relating to quality, safety, and user value, Bing may decide to remove certain results, or we may warn or educate users or provide options for tailoring results.
Spam
Certain pages captured in the Bing index may turn out to be pages of little or no value to users and/or can have characteristics that artificially manipulate the way search and advertising systems work in order to distort their relevance relative to pages that offer more relevant information. Some of these pages include only advertisements and/or links to other websites that contain mostly ads, and no, or only superficial, content relevant to the subject of the search. To improve the search experience for users and deliver more relevant content, Bing might remove such search results, or adjust Bing algorithms to prioritize more useful and relevant pages in search results.”
Furthermore, Koray mentioned that search engines may consider the spammy bad pages as generated by machines.
Google may also consider the pages as unpopular if there is no user demand or no proper user demand.
So, in both cases (Google + Bing’s Perspectives), a large number of pages without user demand put you in an unsafe zone.?
Then Koray read the following questions and told he'll try to answer these:
What is query semantics and Why is it necessary?
How do search engines use Query Semantics?
Why is Query Semantics important for B2B industry?
Why is search demand matter for anti-spam algorithms?
Why do search engines need to variate meanings?
What is the difference between Lexical and Query Semantics?
How to evaluate query semantics for some rare SEO projects?
?
So, Query Semantics means that the meaning of the query changes according to the query language.
The query language concept comes from actually Cranfield experiments. This is one of the first information retrieval researches and it is highly important actually even for today's information retrieval systems. In the cranfield research, here the query language has been used and it is the first actually acceleration of the query language.
Basically, how you search is your query language. In the query language, we don't use meaningful sentences as in the natural language. We just write certain phrases side by side and we expect machine to fill the blank areas which actually brings us to this concept which is uncertain inference. According to uncertain inference, we always need an old space I won't go or dive in here.
Explanation:
Query semantics that means that the meaning of the query changes according to the query language.
Koray emphasizes that query semantics is closely tied to how users phrase their queries (query language).
The Query Language concept comes from Cranfield experiments. This is one of the first information retrieval researches and it is highly important actually even for today's information retrieval systems.
Basically, (from a user’s point of view:) how you search is your query language and in the query language you don't use meaningful sentences in the natural language. You just write certain phrases side by side and expect search engines to fill the blank areas which actually brings us to this concept which is uncertain inference.
Further Explanation:
Query Semantics: This refers to the meaning or intent behind a query, which changes based on how the query is expressed (the query language).
Query Language: It is the formal or informal way in which users frame their queries when searching for information.
What is Query Language?
From a search engine perspective, a language for the specification of procedures for the retrieval (and sometimes also modification) of information from a database.
A query language, also known as data query language or database query language, is a computer language used to make queries in databases and information systems. In database systems, query languages rely on strict theory to retrieve information. A well-known example is the Structured Query Language (SQL).
What is Information Retrieval?
Information Retrieval (IR) is the science and practice of identifying and providing relevant content in response to a user's query from a large collection of data, typically stored in a digital format. IR encompasses the processes, algorithms, and systems used to extract useful information from a vast amount of stored data. This is crucial for search engines as they aim to provide the most accurate and relevant search results to users.
What is Uncertain Inference?
Uncertain inference in the context of search engines refers to the process of drawing conclusions from incomplete, uncertain, or ambiguous data. Search engines must often operate under conditions of uncertainty because they deal with vast amounts of data, complex user queries, and constantly changing information. The challenge is to deliver relevant search results even when the inputs (queries) and the data (web pages and other content) may not be fully clear or complete.
Following are the key concepts of Uncertain Inference.
Handling Ambiguity in Queries:
User queries can be ambiguous or vague, lacking clear intent. For example, the query "Apple" could refer to the fruit, the technology company, or something else entirely. Uncertain inference allows search engines to consider multiple interpretations and either ask for clarification or return a diverse set of results that could match different user intents.
Estimating Relevance:
Given the ambiguity in user queries and the varied nature of content, search engines must estimate the relevance of documents based on uncertain information. This involves probabilistic methods that predict the likelihood of a document being what the user is looking for.
Ranking under Uncertainty:
Search engines must rank results by relevance, even though the relevance itself is not always certain. Techniques such as probabilistic ranking models take into account the uncertainty in data and query interpretation to rank results more effectively.
Learning from User Behavior:
Search engines use data on user interactions (like clicks, time spent on a page, and bounce rates) to infer user satisfaction with search results. This feedback loop, however, is also subject to uncertainty—clicks do not always imply satisfaction, and non-clicks do not always imply dissatisfaction.
Query Semantics means that every query changes its context and the meaning according to the every signal and denotational note or structural synthetic and also semantic unit or lexical unit even their orders can affect actual relevance and search engines have to actually generate questions, sentences or inferences from your queries or queries in the query languages. It is necessary because search engines have to understand the possible contexts. Once they understand these contexts they have to actually prioritize them and then they have to match these contexts that come from queries to the contexts from the documents.
Let's say it five percent, this is the context of the query, but in this document, let's say 30 percent of the document is about this specific context, it means that actually if this section is too early in your document, you will need to lose the rankings.
You have to parse the query as the search engine parses then you have to match every contextual section or unit according to that as well.
Explanation:
Query Semantics means that every query may change its context and the meaning according to:
Even the order of semantic unit or lexical unit can affect actual relevance, and search engines have to actually generate questions, sentences or inferences. It is necessary because search engines have to understand all the possible contexts.
Once search engines understand these contexts, they have to actually prioritize them and then they have to match these contexts that come from queries to the contexts from the documents (webpages).
For example;
Search engine analyzed Query and the Query’s context is about 5% for a given broader topic according to search engine database. Then search engines analyzed a web document (page) and found that about 30% of the total content covers the context that search engine previously found as 5% in the analyzed query.
If that 30% comes earlier in the document that’s a clear topic dilution (extra content/context) so in that case, the analyzed webpage has a high chances of rank drop. You need to understand and parse the query as the search engines to replicate it in your web document with the same contextual and semantic details.
Further Explanation:
Dynamic Context of Queries
Search Engines' Response to Queries: Search engines do not simply match queries to documents based on keywords. Instead, they generate questions, sentences, or inferences from the queries to better understand the underlying intent. This process is crucial for effectively matching queries with the most relevant content.
Understanding and Prioritizing Contexts: Once a search engine understands the possible contexts of a query, it must prioritize these contexts. This means determining which aspects of the query are most crucial for fetching the appropriate search results. This could involve discerning between primary and secondary meanings or focusing on particular semantic nuances indicated by the query structure.
领英推荐
Matching Query Contexts to Document Contexts: The search engine then matches these interpreted contexts of the queries to the contexts within available web documents. For instance, if a query context pertains to a specific topic, the search engine looks for documents where that topic is prominently discussed.
Impact of Content Placement in Documents: The text touches on how the placement of relevant content within a document can affect search rankings. If a significant portion of a document (say 30%) discusses the context related to a query (which might only represent 5% of the query’s focus), and this content appears too early in the document, it may negatively affect rankings. This might be due to the search engine's algorithms favoring a gradual build-up to main topics or penalizing what seems like an over-prioritization or forced insertion of keywords at the beginning of content.
Important Definitions:
What are Semantic Models?
Semantic models, in the context of information systems and computer science, are frameworks that define and organize the relationships between different pieces of data in a way that both humans and machines can understand. They are crucial in various areas, such as database design, artificial intelligence, and particularly in semantic web technologies.
Semantic models aim to capture the meanings and relationships of data within a specific knowledge domain. They go beyond the simple storage and retrieval of data to enable the understanding of how different data entities relate to each other and to real-world concepts.
Important Concepts to Understand Semantic Models:
Entities:
These are the primary objects or concepts within a domain, such as "Person" or "Book" in a library system.
Ontologies:
Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects.
Used extensively in the semantic web, ontologies are rigorous and explicit specifications of conceptualizations. They define the types, properties, and interrelationships of entities in a specific domain. Tools like OWL (Web Ontology Language) are used to create ontologies that enable diverse systems to share and interpret data with common understanding.
Attributes:
Characteristics or properties of entities, such as a person's name or a book's ISBN.
Relationships:
Connections between entities, such as "authored by" linking authors and books.
Constraints:
Rules that govern the relationships and attributes, like "a person must have a unique social security number."
Semantic Networks:
Graph structures used to represent knowledge in patterns of interconnected nodes and arcs, often used in natural language processing and AI to mimic how humans link concepts.
What is Taxonomy?
Taxonomy is a system of classification, especially a hierarchical classification in which entities are classified according to their properties. It is derived from the Greek words taxis, meaning ‘order’ or ‘arrangement’, and nomos, meaning ‘law’ or ‘science’. In the context of Named Entity Recognition, taxonomic classification for entities aids in recognizing the entities. Every entity has a place within a taxonomy for its own entity type. For instance, “continent”, “region”, “country”, “city”, “district”, “street” is a taxonomy instance for certain types of entities.
What is Semantic Unit?
A semantic unit is a basic element of meaning in language. This can be a word, phrase, or any piece of language that conveys a distinct meaning. Semantic units are crucial in semantic analysis, where the goal is to understand the meaning conveyed by words and sentences. For example, in the sentence "The cat sat on the mat," each word represents a semantic unit contributing to the overall meaning of the sentence.
What is Lexical Unit?
A lexical unit refers to a word or a fixed expression that has a single lexical meaning. In simpler terms, it's any entry found in the lexicon (dictionary) of a language, which includes not only individual words but also idioms and fixed phrases. Lexical units are the building blocks of language, each carrying specific meanings that are combined according to grammatical rules to form sentences.
When you input a query into a search engine, the engine analyzes the semantic and lexical units to determine the most relevant results. For example, recognizing "New York" as a single lexical unit rather than two separate words ("New" and "York") affects the search results by focusing on content related to the city rather than new topics.
What is Inference?
Inference refers to the process of drawing conclusions or making decisions based on available evidence, data, or reasoning. It is the process by which search engines deduce the most relevant information from the available data to respond effectively to user queries. This involves interpreting user intent
Following are the key components of inference in Search Engines.
Interpreting User Intent:
Understanding what users mean, rather than just what they say. This involves deciphering whether a query has navigational, informational, or transactional intent. For example, when a user types "New York weather," the search engine infers that the user seeks current weather information rather than historical data about weather in New York.
Query Expansion and Refinement:
Automatically adjusting queries to improve search results. This might include correcting misspellings, synonym replacement (using "automobile" instead of "car"), or adding additional terms to make the query more specific. This helps in matching the query more effectively with relevant content.
Contextual Inference:
Using additional data about the user or the environment to refine search results. This could include location data, the type of device used, previous search history, or current news trends. For instance, a search for "coffee shops" might return results tailored to the user's current location.
Semantic Understanding:
Applying natural language processing techniques to grasp more complex queries, especially those posed as questions or full sentences. This helps the search engine understand the relationships between words in a query, improving its ability to retrieve documents that answer the user’s underlying question.
What is Query Parsing?
Query parsing is a fundamental process by which the user's input (a query) is analyzed and transformed into a structured format that the system can understand and process efficiently. This process is essential for accurately interpreting and responding to user requests.
Query by example here actually they try to take the examples then they try to connect all these samples to the queries and then they try to create a taxonomy between these things so that they can understand what part of toxonomy is a better match for your query you can actually match these things with my example too when it comes to the resource the encryption framework you already know it from the structured data we usually use actual different formats but you can also check this and there are some other examples here in this section too.
If you just check actually W3 they will explain you what a query is why it is important for semantic web you can use actual W3 Consortium as a information Source or basic information Source it will help you you can understand what a triple or what a pattern is here and with that said this is the Google research publication that I just showed you there and at the same time when it comes to the semantic queries this is also important in a way because sometimes you can use these knowledge spaces some of these knowledge spaces are actually for creating applications but in this case if you want to create let's say you want to understand the search engines it doesn't mean that you should create one but at least you can try to have an empath with the search engine Engineers that's why these knowledge pages are actually important before coming to that area actually.
Explanation:
Koray discusses following sources to consult:
Then, the following Microsoft blog post was discussed in the upcoming content:
There is also an important blog post again from the Microsoft Bing to explain how AI helped the Bing to have better query understanding and there are some samples here.
For example, when you search for “Windows update reset tool” before and after they try to show actual how the results change according to the semantic relevance and they also explain how these things actually can be used in other cognitive search as well and here actually they also explain how you can create your own semantic search engine and how you can weight the different types of places on the web pages or how you can actually parse the queries and Etc is up to your own configuration.
Explanation:
Koray discusses advancements in search engine technology, particularly focusing on Microsoft Bing's use of artificial intelligence (AI) to enhance query understanding.
Here's an explanation of the key points covered:
AI's Role in Bing's Query Understanding: Koray highlights a blog post by Microsoft Bing that explains how AI has been instrumental in improving the search engine's ability to understand and process user queries. AI technologies enable Bing to parse and interpret search queries more effectively, adapting to the nuances of language used by searchers.
Demonstration of Semantic Relevance: The blog post apparently includes examples that illustrate the impact of AI on search results. For instance, the example of "Windows update reset tool" is used to show how search results have improved in terms of semantic relevance before and after the integration of AI technologies. This likely means that Bing can better grasp the intent behind queries and fetch more relevant and accurate results based on that understanding.
Application in Cognitive Search: Koray also mentions that the insights from using AI in enhancing query understanding are applicable to broader areas known as cognitive search. Cognitive search involves using AI to provide more intuitive search experiences, enabling the search engine to think more like a human when processing queries. This includes understanding context, intent, and even subtleties in the language that traditional keyword-based search might miss.
??
Then, the following Microsoft blog post was discussed in the upcoming content:
There is another post to understand the query semantics because When it comes to the query semantics you have to understand that a word will definitely change its meaning from in a dictionary if let's say something is synonym with something it doesn't mean that it will be the same in the query language sometimes it might sometimes it might not be sometimes it might be in that way if a third another concept appears them appears there with together with them.
Explanation:
Koray emphasizes that in query semantics, the meaning of a word changes from its traditional dictionary definition based on the context provided by other words in the query.
A word that is considered synonymous with another in the dictionary may not have the same meaning in query semantics.
The presence of additional concepts or terms in the query can influence or alter the meaning of words. For instance, "how to reset Windows" is distinct from "how to reset Windows update tool." The addition of "update tool" modifies the context, changing the semantic interpretation of "reset Windows."
So here when you search for ‘how long does canned soda last’, ‘canned diet soda’, ‘soft drinks’, also come there, ‘unopened room temperature pop’ also comes there, ‘carbonate drinks’ come there so according to the Microsoft being canned soda and these things are synonyms to each other or interchangeable phrases so in this case you have to understand that actually these relevance's actually also come from word2vec models and they try to understand what is the canonical query according to the query format and at the same time Concepts in the query.
Explanation:
Here, Koray explains the way search engines, specifically Microsoft Bing, interpret and process user queries to find relevant information.
Here’s an explanation of the key concepts mentioned:
Search Query Interpretation: The example given ('how long does canned soda last') illustrates how search engines do not just look for exact matches to the query terms but expand the search to include various related terms and phrases. In this case, related searches include 'canned diet soda', 'soft drinks', 'unopened room temperature pop', and 'carbonate drinks'. This demonstrates how search engines understand that these terms might be relevant to the user's intent despite the differences in wording.
Synonyms and Interchangeable Phrases: Microsoft Bing treats phrases like 'canned soda' and the others mentioned as synonyms or interchangeable in the context of search queries. This means the search engine recognizes that a user searching for one of these terms may be satisfied with information relevant to any of the others. This is crucial for delivering comprehensive and relevant search results.
Relevance and Word2Vec Models: The reference to 'word2vec models' points to a method used by search engines to understand the semantic relationships between words. Word2Vec is a type of machine learning model that learns to associate words with other words in similar contexts. It creates vector representations of words in a space where words that share common contexts in the corpus are located close to one another. Thus, 'canned soda' and 'soft drinks' may be closely positioned in this vector space, indicating their relevance to similar search queries.
Canonical Query Identification: Koray also mentions the concept of identifying a 'canonical query'. This refers to the process by which a search engine determines the most representative or "standard" form of a query that encompasses various ways people might phrase similar questions. The canonical form helps streamline the search process and improves the efficiency of retrieving relevant documents by reducing the number of unique queries the system needs to handle directly.
Understanding Concepts in Queries: Finally, the reference to understanding concepts in the query suggests that search engines are not only parsing individual words but are also analyzing the overall concept or intent behind the query. This higher-level understanding is essential for providing search results that are truly useful to the user, beyond mere keyword matching.
There are other things here to explain the character embeddings if you want to understand the prominence of the character embedding I would suggest you to check these two parts it will be helpful to understand how it increases the precision even further so I believe these sections explain them but doesn't end there because enriching query semantics for code search, the code search it goes richer every year and now even actually Google for Transformers or Google language models the code generation is another NLP task or language model quality task or Benchmark as regular natural text Generations in other words when you say something the machine needs to write the code in a certain programming language so this is about that and how we can actually match or transfer human language the code language or programming language this is about that and query someone thinks here it is needed because if someone searches or if someone actually tries to generate code as in the query semantics it will be harder and here they try to understand the semantic gap between them.
Explanation:
Koray explains here some advanced topics in the field of Natural Language Processing (NLP), specifically focusing on character embeddings, code search, and the use of language models like Transformers in code generation.
Here's an explanation of the key concepts mentioned:
Character Embeddings: Character embeddings are a type of representation in NLP where individual characters are represented as vectors in a high-dimensional space. This approach helps in understanding the prominence or importance of each character within a larger text, which can significantly enhance the precision of language models by capturing more detailed linguistic patterns than word-level embeddings alone. The mention of checking two parts likely refers to specific sections or resources that explain how character embeddings work and their impact on NLP tasks.
Enriching Query Semantics for Code Search: Code search involves querying a database of code snippets or programs to find pieces of code that match a user’s intent. Enriching the query semantics in the context of code search means improving the ability of search tools to understand more complex or nuanced queries about programming tasks. This is becoming increasingly sophisticated with the improvement of NLP techniques, allowing for a richer understanding of programming-related queries.
Code Generation with Language Models: The text references the use of language models like Google’s Transformers for code generation, which is an NLP task where models generate snippets of code based on human language inputs. This requires the model to translate regular language queries into programming language, which is a complex task due to the need to not only understand the syntax of the programming language but also the semantic intent behind the user’s query.
Benchmarking Language Model Quality: Discussing benchmarks in the context of regular text and code generations implies evaluating and comparing the performance of language models in generating both natural language texts and programming code. Benchmarks help in assessing how well these models handle different types of language tasks, including their ability to bridge the semantic gap between human language and code.
Addressing the Semantic Gap: The semantic gap refers to the difference between the way humans describe programming tasks in natural language and the specific expressions in programming languages required to execute these tasks. Bridging this gap is crucial for effective code generation and enhancing the interaction between users (who might not be programmers) and software development tools.
Importance of Query Semantics: Finally, Koray explains the importance of refining query semantics in the context of code generation. As the technology evolves, ensuring that search engines or language models can accurately interpret the intent behind queries for code generation becomes more challenging. The better these systems understand the semantics of both natural and programming languages, the more effectively they can serve users who are trying to generate or find specific pieces of code based on conversational or non-technical descriptions.
Why Query Semantics is necessary?
Search engines will need to associate cluster and also predict every meaning, every context of all these possible inferences that come from the queries, then they will need to distribute these possibilities to the audiences according to the user demand.
They will need to match the documents according to that and sometimes document popularity might affect the query context if there is not enough level of user Behavior or user data so that because search engine will assume that if the query search demand increases then this type of document suddenly appears it means that these documents are directly for these type of queries as well.
So in this case when I create my own content briefs or semantic content networks I have to remember this type of possibilities so that I can put my source in front of others especially for these type of seasonal SEO events because they will show who is more quality and who is more needed on the SERP or on the web.
Explanation:
Here, Koray discusses why query semantics is important and explains how search engines handle and interpret search queries to provide the most relevant semantically optimized results.
Here's a detailed breakdown of the key points:
Associating, Clustering, and Predicting Contexts: Search engines must discern and predict the meanings and contexts of words within user queries. This involves creating associations between different queries and the contexts they might represent. Clustering involves grouping similar queries to understand common user intents better.
Distribution According to User Demand: Search engines distribute query results based on user demand, ensuring that the most relevant and demanded content reaches the appropriate audience. This adaptability helps in tailoring search results to fit the collective or individual preferences of users, enhancing the overall search experience.
Matching Documents to Queries: Beyond just understanding queries, search engines need to match these queries with the most relevant documents. This process includes evaluating the content of documents to ensure they meet the inferred needs based on the query context.
Influence of Document Popularity: The popularity of documents can influence their relevance to certain queries, especially if there is limited user behavior data available. For instance, if a specific type of document suddenly becomes popular following an increase in certain queries, search engines might assume these documents are particularly relevant to these queries. This is particularly noticeable with trending topics or sudden news events, where new information might become highly relevant temporarily.
Implications for Content Creation: When creating content briefs or semantic content networks, it's crucial to consider these dynamics. Understanding how search engines associate query contexts with document relevance can help in positioning content effectively to meet these inferred needs. This is especially important for seasonal SEO events or topics that are subject to fluctuations in popularity, as these are opportunities to demonstrate the quality and relevance of the content to the search engines and users.
Strategic SEO Planning
Why the search engines need to variate the meaning of the phrases in the query?
First of all in the query, every word has multiple other types of meanings and a search engine needs to always actually change the meaning of these things because the context always changes.
So a word might have nine different contexts but when you put another word before or after it, there might appear some context constraints and according to these context constraints, the search engine will need to start actually filter these documents but serving all these documents faster or faster and faster is more and more important.
In this case, a search engine might start to realize that instead of filtering these things out already, we can actually register every document to a different indexing chart or index partition. So, in this case, we can actually store them faster. It means that actually documents might change their places in the indexes or indices and this might happen according to the query context so and that's why actually having a contextual consolidation is really important.
Explanation:
Koray explains here why search engine need to variate the meaning of a query. He addresses the adaptive nature of search engines in handling the varying meanings of words within queries, based on the context in which they are used.
Here's a breakdown of the key points explained:
Multiple Meanings of Words: In any given query, words can have multiple meanings, which is a fundamental challenge in search technology. The context in which a word is used can drastically alter its meaning. For instance, the word "apple" could refer to the fruit or the technology company, depending on additional words in the query or the user's search history and behavior.
Contextual Variations: When words are combined in queries, they create specific contexts that can constrain their meanings. For example, placing "red" before "apple" typically narrows the interpretation to the fruit rather than the company. Search engines must adapt to these context clues to deliver relevant results.
Filtering and Index Partitioning: To handle these variations efficiently, search engines need to filter through documents rapidly. The explanation suggests a strategy where instead of just filtering out non-relevant documents after a query is made, search engines might use a method called "index partitioning." This involves organizing documents in the index based on different potential meanings and contexts beforehand.
Faster Document Retrieval: By registering each document under different index partitions according to potential query contexts, search engines can retrieve relevant documents more quickly. This approach enhances the speed of search results because the engine can directly access the partition that is most relevant to the interpreted query context, rather than filtering through a larger, more general index.
Dynamic Index Reorganization: As the understanding of context or the common usage of words changes, documents might shift within these partitions or indexes. This reorganization ensures that the search engine remains accurate and efficient in fetching documents as language use and contextual associations evolve.
Importance of Contextual Consolidation: Then, Koray mentions the importance of "contextual consolidation" in SEO. This refers to the need for content creators and SEO strategists to ensure that their content is closely aligned with the possible interpretations and contexts of the keywords they target. Doing so helps their content remain relevant across different partitions and improves its visibility under varying query contexts.
?
Then, the following Research Study was Discussed in the upcoming content:
A research paper; ‘Searching with Context’ here it actually explains many different types of possibilities and explanations here I would suggest you to read this document or this paper to be honest because especially ‘Rank Biasing’, ‘Query Rewriting’, and ‘Query Rank-Biasing’. ?
Besides that, how this ‘Iterative, Filtering-Meta Search’ processes how they can actually change ranking with every iteration. It is also important for understanding the cost of these things as well and also it is important because sometimes search engines might not choose every word in the query to rank the results.
Sometimes, even if you delete a word from the query results don't change right. So, it means that actually it is not a contextual word or it is not a necessary word it doesn't have a weight in the query. Here they Explain how other queries can be generated according to the context.
Explanation:
Koray discusses here “Searching with Context” research study highlighting the important concepts.
Here's a breakdown of the key concepts and their significance in understanding how search engines work:
Rank Biasing: This concept refers to the practice of modifying search results based on certain biases or preferences inherent in the search engine's algorithms. For example, a search engine might prioritize certain websites over others based on their perceived authority or relevance to a specific type of query.
Query Rewriting: This involves the search engine automatically modifying a user's search query to produce more relevant results. Query rewriting can include expanding the query with additional terms, simplifying it, or correcting errors. This helps in refining the search to better match the user's intent even if their original query was vague or ambiguous.
Query Rank-Biasing: Similar to rank biasing, query rank-biasing specifically refers to adjusting the ranking of search results based on modifications or interpretations of the query itself. This might involve prioritizing certain aspects of a query over others based on what the search engine deems most relevant.
Iterative, Filtering-Meta Search Processes: This describes a method where the search process is iterative, meaning it repeats several times with adjustments at each step. Each iteration filters the results further, refining the search outputs based on new criteria or insights gained from previous search results. This iterative process helps in fine-tuning the search results to closely match what the user is likely seeking.
Cost of Search Operations: Koray also points out the operational costs involved in these sophisticated search processes. This includes computational costs and the cost of potential inaccuracies in user experience if the search does not align well with user expectations.
Non-Contextual Words in Queries: Koray mentions here how some words in a query may not significantly impact the search results, indicating that these words do not carry contextual weight. Understanding which words are essential and which are not can help SEO efforts in focusing on terms that truly influence rankings.
Generation of Context-Based Queries: Koray, here, points out how understanding the context can lead to the generation of new queries that are aligned with the user’s intent. This aspect is crucial for search engines to adapt and respond to the nuanced needs of users, improving the relevance and precision of the search results.
Difference between lexical and query semantics is that; the lexical semantics come from natural language and query semantics come from search language or search query language. In this case, as I said already; ‘selling’ and ‘buying’ might be antonyms in the dictionary but they are synonyms in the query semantics.
Explanation:
Koray, here, distinguishes between lexical semantics and query semantics.
Lexical Semantics: This refers to the meaning of words as understood in natural language, independent of any specific context. Lexical semantics focuses on the dictionary definitions of words and their relationships with other words, such as synonyms, antonyms, and hyponyms. It is a fundamental aspect of linguistics that deals with how words convey meaning on their own.
Query Semantics: Unlike lexical semantics, query semantics derives from the context of search queries, reflecting how words are used and understood within the framework of search engines. This concept recognizes that the intent behind search queries can align words typically seen as antonyms in lexical terms as synonyms in the context of search behavior.
The example given—'selling' and 'buying'—illustrates this distinction vividly. In traditional lexical semantics, these terms are antonyms: one refers to giving something in exchange for money, and the other to receiving something in exchange for money. However, in the context of query semantics, both terms can be aligned closely in meaning. When users search for these terms, they may be part of the same customer journey or reflect similar needs, such as a user looking to engage in a transaction either as a buyer or a seller. Therefore, in the realm of search engines, these terms might be treated as synonyms because they serve similar user intents, such as finding a marketplace or platform for transactions.
This distinction is crucial for SEO because it emphasizes the need to understand the searcher's intent, not just the literal meaning of their words. Optimizing content for query semantics involves anticipating the various ways users might express their search intentions and how these expressions relate to each other within the broader context of search activities. This approach ensures that SEO strategies are more aligned with actual user behavior and the operational logic of search engines, thereby improving content visibility and relevance in search results.
When it comes to using all these information actually for SEO or search engine optimization, you will need to use actually this type of understandings or these type of approaches to create or design your website because you can't directly create a website for only b2c because b2c or B2B. You will need to calculate all these possibilities and you will need to augment your topical map if Google infers a query in a different way and if they augment the meaning of the query it means that you will need to augment your topical map too you might even need to change some certain sections of your business model or business identity or Source context because in helpful content update announcement to Google site that actually who you are and why do you explain these things so your identity and topic should be relevant to each other if you change your if you change the queries that you want to rank it should be connected to your Source or your identity and to be able to provide that you need to position your identity or the brand according to that as well.
Explanation:
Koray, here, emphasizes on the importance of flexible, adaptive SEO strategies that respond to the evolving ways search engines interpret queries.
Here's a breakdown of the key concepts discussed:
Understanding SEO: The core message is that SEO is not just about targeting direct business to consumer (B2C) or business to business (B2B) models but involves a comprehensive understanding of various possible interpretations by search engines. This requires an adaptable approach to website design and content structuring.
Topical Maps and SEO Strategy: As search engines like Google refine their understanding of queries, possibly changing their interpretation over time, it necessitates corresponding updates to a website's topical map. A topical map organizes content thematically to enhance the site’s relevance and authority on specific topics. Adapting topical maps to how search engines reinterpret queries is crucial to maintaining or improving search rankings.
Business Model and Identity Alignment: Koray explains the need for a business’s online identity and model to stay aligned with the evolving topical maps. Changes in search queries that you aim to rank for should reflect your business identity and the source of the content. This alignment ensures that the content remains relevant and authoritative as per the search engine's criteria.
Google's Helpful Content Update: Koray highlights the search engine's focus on the relevance of content to the searcher's intent. Websites need to clearly articulate who they are and why they are discussing certain topics to rank well. This is a shift towards valuing user-centric and purpose-driven content on the web.
Positioning Identity or Brand: According to Koray, it's essential for businesses to position their identity or brand in a way that matches the topical focus they are aiming for. This strategic positioning helps ensure that the content not only ranks well but is also perceived as authoritative and genuine by both users and search engines.
-----
Koray's Case Study Ended Here. Thanks for Reading it thoroughly.
Ask your questions in the comment section and I'll try my best to answer.
-----
Important Note:
Koray showed these websites (Myros & Civar) from a Case Study saved in Google Documents.
Title of Case Study: Importance of Information Responsiveness for SEO: How to Balance PageRank?
Koray also mentioned in the video that this case study will be published already when you’ll be watching this video.
He further said that he published that case study for balancing the page rank by explaining:
-----
I’ve searched for that case study over the internet that Koray is showing in this Query Semantics Case Study video.
But this case study has not been published yet.
-----
However, Koray has mentioned in one of his case studies that the mentioned case study will be published in Book Form along with two other case studies.
Following is the snippet from that article:
?“Information Responsiveness” and “Cost-of-Retrieval” will be explained later with case studies published in book form with the names below.
Case Study: How to Expand a Topical Map for Higher Topical Authority? https://www.holisticseo.digital/seo-research-study/topical-map
Published on: January 5, 2023 By Koray Tugberk Gubur
-------------
While searching this “Importance of Information Responsiveness” case study mentioned in Query Semantics Video, I tried to contact some of Koray’s students, who have attended Koray’s Topical Authority Course, to enquire if anyone knows about this case study.
Thanks to Muhammad Hamid Khan who replied and suggested to read “Verbs of Life” case study.
“Understanding the Verbs of Life” case study contains a small portion about Query Semantics that we've not included here due to the length of the this case study.
-------------
Special Credits:
Sales And Marketing Specialist at AIO Hospitality Solutions
6 个月Thanks for the indepth explanation
SEO Expert | E-commerce, SaaS, B2B and B2C to Generate more leads through SEO and Content Marketing.
7 个月what is said "Like Father (Koray Tugberk GUBUR) Like Son (Behzad Hussain) - Thank you very much for the effort you put into explaining query semantics. Feeling proud of you. Masha ALLAH.
Founder at FanCanvas
9 个月Many?thanks
Semantic SEO Specialist | Content Strategist | Outreach Link Builder | SEO PR Link Builder | Topical Authority | AI Enthusiast |
9 个月Great share. Very informative and definitely you did hard work to make it for you and us. Please continue this series for all of us. Thanks a lot bro. Habibe
SEO Content Writer | Expert in Creating Engaging Content | Digital Marketing |
10 个月I have read it. I have gained a better understanding of semantic SEO from your clear explanations. Well done on conveying the concepts effectively! ????