14 Jan Latent Semantic Indexing (LSI): Is It A Google Ranking Factor?
Is it true that “sprinkling” phrases that are closely similar to your goal keyword will help you rank higher? These are the benefits and drawbacks of using LSI as a ranking criterion.
Latent semantic indexing (LSI) is a method for identifying patterns in the relationships between phrases and concepts through indexing and information retrieval.
A mathematical approach called LSI is used to locate semantically related terms inside a collection of text (an index) that would otherwise be buried (or latent).
And in that light, this appears to be crucial for SEO.
After all, Google is an extensive database, and we’ve been hearing a lot about semantic search and the importance of relevance in the search ranking algorithm.
You’re not alone if you’ve heard about latent semantic indexing in SEO or been told to employ LSI keywords.
On the other hand, will LSI assist you in increasing your search rankings? Let’s have a look at what we’ve got.
The Claims: Latent Semantic Indexing Can Be Used As A Ranking Factor
The premise is straightforward: employing LSI keywords to optimize web content helps Google better understand it, and you’ll be rewarded with higher rankings.
You can improve Google’s interpretation of your content by employing contextually related terms. So goes the story.
Following that, the resource makes some rather persuasive arguments in favor of LSI keywords:
• To analyze text at such a deep level, Google relies on LSI keywords.”
• “LSI Keywords are NOT the same as synonyms.” Instead, they’re similarly related terms to your goal keyword.”
• “Only terms that perfectly match what you just searched for are bolded on Google” (in search results). They also capitalize comparable words and phrases. These are LSI keywords that you should use liberally throughout your content.”
Evidence In Support Of LSI As A Ranking Factor
One of the five primary characteristics used by Google to determine which result is the best answer for each given query is relevance.
The “most basic signal” of relevancy, according to Google, is that the terms used in the search query exist on the page. That makes sense; how could Google know you’re the best response if you’re not using the terms the searcher is looking for?
This is where LSI, according to some, comes into play.
If employing keywords is a sign of relevancy, choosing the appropriate keywords must be much more so.
There are specific tools to assist you in locating these LSI keywords, and proponents of the strategy propose employing a variety of other keyword research techniques to find them as well.
The Case Against LSI As A Ranking Criteria
In SEO, there’s a healthy mistrust that Google will say things that will lead us astray to maintain the algorithm’s integrity. So let’s get started.
First and foremost, it’s critical to comprehend what LSI is and where it comes from.
In the late 1980s, latent semantic structure arose to recover textual items from computer files. As a result, it’s an early example of an information retrieval (IR) notion available to programmers.
It got more challenging to find exactly what one was seeking in a collection as computer storage capacity improved and electronically available data sets expanded in size.
In a patent application filed on September 15, 1988, researchers highlighted the challenge they were attempting to solve: “Most systems still require a user or information provider to declare explicit relationships and links between data objects or text objects, making them difficult to use or apply to big, heterogeneous computer information files whose content may be unfamiliar.”
Keyword matching was in use in IR at the time, but its flaws were apparent long before Google arrived.
Too often, the terms a person typed into a search engine were no exact matches for the words found in the indexed data.
This is due to two factors:
• Synonymy: the wide range of terms used to describe a particular item or idea leads to the omission of relevant results.
• Polysemy occurs when a single term has many meanings, resulting in the retrieval of irrelevant results.
These problems persist today, and you can imagine how much of a nuisance they are for Google.
However, Google’s relevance-solving methodology and technology have moved on from LSI long since.
LSI created a “semantic space” for retrieving information on its own.
According to the patent, LSI considered the inaccuracy of association data to be a statistical issue.
Even if there is no exact keyword match, doing so would disclose the latent meaning and allow the engine to return more relevant results — and just the most relevant ones.
Google’s index has hundreds of billions of pages and is constantly increasing.
When a user types a query into Google, the search engine sorts through its database in a fraction of a second to find the best answer.
Using the methods outlined above in the algorithm would necessitate Google:
1. Using LSA, recreate that semantic space throughout the entire index.
2. Examine the query’s semantic meaning.
3. In the semantic space formed by examining the entire index, find all similarities between documents and the semantic meaning of the query.
4. Sort and rank the outcomes.
That’s an exaggeration, but the point is that this isn’t a scalable procedure.
This would be handy for little information sets. For example, it helped find important reports within a company’s electronic collection of technical material.
Using a set of nine documents, the patent application demonstrates how LSI works. That’s precisely what it was made to do. In terms of automated information retrieval, LSI is rudimentary.
Our Opinion On Latent Semantic Indexing As A Ranking Factor
While the core ideas of removing noise by establishing semantic relevance have undoubtedly influenced improvements in search ranking since LSA/LSI was trademarked, LSI is no longer relevant in SEO.
Although it hasn’t been entirely ruled out, there is no proof that Google has ever utilized LSI to rank results. Today, neither LSI nor LSI keywords are used by Google to rank search results.
Those who advocate for the use of LSI keywords are grasping at a notion they don’t fully get in an attempt to explain why how words are related (or not) is significant in Local SEO.
The core considerations in Google’s search ranking algorithm are relevance and intent.
Those are two essential questions they’re attempting to address to find the best answer to every query.
Synonymy and polysemy remain significant obstacles.
Semantics, or our grasp of the multiple meanings of words and their relationships, is critical for providing more relevant search results. LSI, on the other hand, has nothing to do with this.
