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Latent Semantic Indexing and Search Engines Optimimization (SEO)

Posted on April 14, 2007
Filed Under For The Record, Google, Internet Marketing, Make Money Online |

In 2006, Google incorporated latent semantic indexing into its algorithms. LSI is a vector based tool which uses a mathematical formula to determine if the content of the article it is crawling is relevant to both the site and article.

I am currently conducting an experiment with an article that utilizes a method I have read about. The article is written with what Google thinks is relevant to the theme, and I will post the article the first of the week to see how long it will take to reach the top of the engines. I usually have good luck with my articles ranking high, but this is an entirely new method for me. i will be documenting every aspect of this experiment, and if it works as well as I think it will, I will post the results including timelines to FTR. Look for results soon.

Drive on…

Peace,

Charlie~

Jose Nunez

Latent Semantic Indexing and Search Engines Optimimization (SEO)
By Jose Nu�ez

The closest search engines have come to actual applications of this technology so far is know as ‘Associative Indexing’ and it is put in effect under Stemming, or the indexing of words on the basis of their uninflected roots (plurals, adverbs, and adjectival forms are reduced to simple noun and verb forms before indexing).

Latent Semantic Analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, invented in 1990 [1] by Scott Deerwester, Susan Dumais, George Furnas, Thomas Landauer, and Richard Harshman. In the context of its application to information retrieval, it is sometimes called Latent Semantic Indexing (LSI).

Here are some quick facts about Latent Semantic Indexing: 1. LSI is 30% more effective than popular word matching methods. 2. LSI uses a fully automatic statistical method (Singular Value Decomposition) 3. It is very effective in cross-languages retrievals. 5. LSI can retrieve relevant information that does not contain query words. 6. It finds more relevant information than other methods.

Latent Semantic Indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method examines document collections as a whole, to see which others do contain some of those same words. LSI considers documents that have many words in common to be semantically close, and ones that have few words in common to be semantically distant. This method correlates surprisingly well with how a human being looking at content, classifies multiple documents.
Jose Nu�ez is a Scientific SEO/SEM Specialist. He also also owns and operates HiRank an online resource focusing on Search Engines (SE) and Artificial Inteligence (AI)

Find out more at: http://www.hirank.com/ Or contact him at: jnunez@hirank.com

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