View source for Latent Semantic Indexing

<seo title="What is Latent Semantic Indexing (LSI)?" metadescription="Latent Semantic Indexing (LSI) is a method used to improve information retrieval by identifying relationships between terms used in a document. Learn more…" />
 
== Latent Semantic Indexing – Definition & explanation ==
[[File:Latent-Semantic-Indexing.png|thumb|450px|right|alt=Latent Semantic Indexing|'''Figure:''' Latent Semantic Indexing - Author: Seobility - License: [[Creative Commons License BY-SA 4.0|CC BY-SA 4.0]]|link=https://www.seobility.net/en/wiki/images/e/ef/Latent-Semantic-Indexing.png]]

Latent Semantic Indexing (LSI), also called Latent Semantic Analysis, is a method used to improve information retrieval by identifying relationships between terms used in a document. Latent semantic indexing can be used in [[Natural Language Processing|natural language processing]] to improve the understanding that computers have of a piece of content.

In SEO, LSI is used by some to help improve their [[On-Page SEO|on-page SEO]], helping them find relevant supporting terms to cover on their pages. It can also be used to find relevant content ideas or information that could be added to a page in order to make it more relevant.
 
[[File:Tf-idf-weighting.png|link=|border|750px|alt=Latent Semantic Indexing Screenshot|Screenshot of Seobility's TF*IDF tool]]

Screenshot of [https://www.seobility.net/en/wdf-idf-tool/ seobility.net] showing an example of various words that are semantically related to the main keywords “SEO”
 
== LSI & Search ==

In the early days of search, [[Search Engine|search engines]] like Google relied solely on [[Keyword|keywords]] in order to judge the relevance of content. This came with a variety of problems, including mass [[Keyword Stuffing|keyword stuffing]] by website owners in a bid to improve their [[SERP|SERP]] rankings.

As search algorithms adapted, aspects of natural language processing were introduced to take focus away from exact match keywords and keyword density. It was thought that LSI was a big part of this transition, although it was never confirmed by Google. This caused SEOs to place more focus on related keywords and writing content for visitors instead of search engines.

== Its use today ==

Today, LSI is used as a guideline by some SEOs as opposed to being seen as an actual ranking factor, with many not using it at all. It can be used to uncover related terms that can enhance the topical relevance of a piece of content.

Another use for LSI is to uncover potential gaps within the content of a post. By better understanding what terms are relevant to a page’s main keyword, it’s possible to improve the quality of the content.
 
== LSI keywords ==

“LSI keywords” is a term used to describe words that are semantically related to the main keyword of a page. These words help add contextual relevance to a piece of content, potentially allowing search engines to better understand what the content is about.

This is especially relevant when the main keyword can have different meanings, or when words within a key phrase have strong differing meanings when used alone.

So-called LSI keywords aren’t the same as synonyms, which are words with the same or nearly the same meaning as the main keyword.

== Relevance to SEO ==

Although LSI was used for optimizing webpages in the past, today, it’s not used as often. However, it can still be used to provide an indication as to how relevant content is, as well as provide information on topics that can help improve the depth of content.
 
== Related links ==

* https://www.searchenginejournal.com/latent-semantic-indexing-wont-help-seo/240705/
* https://lsigraph.com/
 
== Similar articles ==

* [[Keyword]]
* [[Natural Language Processing]]

[[Category:Search Engine Optimization]]

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