Latent Semantic Indexing (LSI) is an indexing and information retrieval method for identifying relational patterns between terms and concepts.
In LSI, a mathematical technique is used to find semantically related terms in the text collection (a index) these relationships may be hidden (or latent).
In this case, it sounds like it could be very important for SEO.
right?
After all, Google is a huge index of information, and we hear all kinds of Semantic search and The importance of relevance in the search ranking algorithm.
If you’ve heard rumors about latent semantic indexing in SEO or been suggested LSI keywords, you’re not alone.
But will LSI really help you improve your search rankings? Let’s see.
Claim: Latent Semantic Indexing as a Ranking Factor
The statement is simple: Optimizing web content with LSI keywords helps Google understand it better, and you’ll rank higher.
Backlinko defines LSI keywords in this way:
“LSI (Latent Semantic Indexing) keywords are conceptually related terms that search engines use to gain insight into what’s on a web page.”
By using contextual terms, you can deepen Google’s understanding of your content. The story goes like this.
This resource goes on to make some very compelling arguments for LSI keywords:
- “Google relies on LSI keywords to understand contentt on such a deep level. “
- “LSI keywords are not synonyms. Instead, they are terms closely related to your target keywords. “
- “Google doesn’t just offer exact matches for bold words What you just searched for (in search results). They also bolded similar words and phrases. Needless to say, these are all LSI keywords you want to add to your content. “
Does this practice of “spreading” terms closely related to your target keywords help improve your rankings with LSI?
Evidence of LSI as a ranking factor
Relevance is considered one of the five key factors that help Google determine which result is the best answer to any given query.
as google explains How Search Works resource:
“To return relevant results for your query, we first need to determine what information you’re looking for — the intent behind the query.”
Once the intent is determined:
“…algorithms analyze the content of web pages to assess whether the page contains information that may be relevant to what you are looking for.”
Google goes on to explain that the “most basic signal” of relevance is the presence on the page of a keyword used in a search query. It makes sense – if you’re not using the keywords the searcher is looking for, how can Google tell you that’s the best answer?
Now, this is where some people think LSI comes into play.
If using the keyword is a correlation signal, use the right keywords Should be a stronger signal.
There are dedicated tools designed to help you find these LSI keywords, and believers of this strategy also recommend using a variety of other keyword research strategies to identify them.
Evidence against LSI as a ranking factor
Google’s John Mueller crystal clear at this:
“…we don’t have the concept of LSI keywords. So that’s something you can ignore completely.”
There is a healthy skepticism about SEO that Google might say something that leads us astray in order to protect the integrity of its algorithms. So let’s dig here.
First, it’s important to understand what LSI is and where it comes from.
In the late 1980s, latent semantic structure emerged as a method for retrieving textual objects from files stored in computer systems. As such, it is an example of one of the early Information Retrieval (IR) concepts available to programmers.
As computer storage capacity increases and the size of electronically available datasets continues to expand, it becomes more difficult to find exactly what people are looking for in that collection.
Researchers describe the problem they are trying to solve patent application Submitted September 15, 1988:
“Most systems still require the user or information provider to specify explicit relationships and links between data objects or text objects, making the system difficult when used or applied to large heterogeneous computer information files whose content may be unfamiliar to users. tedious.”
Keyword matching was used in IR at the time, but its limitations were obvious long before Google came along.
Many times, the words a person uses to search for the information they seek do not exactly match the words used in the indexed information.
There are two reasons:
- synonym: Various words used to describe a single object or idea cause related results to be missed.
- polysemy: Different meanings of a single word lead to irrelevant results.
These are still issues today, and you can imagine what a headache this was for Google.
However, the methods and techniques used by Google to solve the correlation problem were transferred from LSI long ago.
What LSI does is automatically create a “semantic space” for information retrieval.
As explained in the patent, LSI treats this unreliability of linked data as a statistical problem.
Without paying too much attention to weeds, these researchers basically think there is a hidden underlying semantic structure that they can tease out from word usage data.
Doing so will reveal underlying implications and enable the system to bring back more relevant results – and if only The most relevant results – even if there are no exact keywords.
Here’s what the LSI process actually looks like:
The most important thing you should note about the above description of the method in the patent application is that two separate processes take place.
First, a collection or index undergoes latent semantic analysis.
Second, analyze the query and then search the processed index for similarities.
This is the fundamental problem with LSI as a Google search ranking signal.
Google’s index is Lots of exist 100 billion The number of pages is still growing.
Every time a user enters a query, Google sorts its index in fractions of a second to find the best answer.
Using the above method in an algorithm would ask Google to:
- Recreate the semantic space Use LSA throughout the index.
- Analytical Semantics query.
- Find all similarities between the semantic meaning of the query As well as documents in the semantic space created by analyzing the entire index.
- Sort and Rank those results.
This is a gross oversimplification, but the point is that this is not a scalable process.
This is useful for small collections of information. For example, it helps to display relevant reports in a company’s computerized technical documentation archive.
The patent application illustrates how LSI works using a collection of nine documents. That’s what it’s designed for. LSI is the original in computerized information retrieval.
Latent Semantic Indexing as a Ranking Factor: Our Verdict
The basic principle of removing noise by determining semantic relevance has certainly influenced the evolution of search rankings since LSA/LSI was patented, but LSI itself has no useful application in SEO today.
Not entirely ruled out, but there is no evidence that Google ever used LSI to rank results. Google absolutely does not use LSI or LSI keywords to rank search results today.
Those who recommend LSI keywords are grasping at a concept they don’t quite understand in an effort to explain why the way words are related (or not) is important in SEO.
Relevance and intent are fundamental considerations in Google’s search ranking algorithm.
These are the two big problems they try to solve when it comes to providing the best answer to any query.
Synonyms and polysemy remain major challenges.
semantics — that is, our understanding of the various meanings of words and how they relate to each other — is critical to producing more relevant search results.
But LSI has nothing to do with it.
Featured image: Paulo Bobita/Search Engine Magazine
