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Latent Semantic Indexing
Overview of latent semantic indexing and how it relates to search engine optimization
Keyword density, search engine optimization, and reciprocal links have long been essential elements for More...getting websites ranked high in search engines. But with rising numbers of sites created for the sole purpose of targeting keywords without valuable content, new methods have became necessary to separate sites with relevant information from empty shells. Latent semantic indexing presents the idea of ranking sites by theme rather than keyword.
Ranking by Theme
By including related words, latent semantic indexing is meant to allow a more natural approach for search engines to view and rank websites. This changes the approach from focusing on keyword usage to looking for themes. In essence, LSI can be used to identify websites which contain information visitors will find relevant to their specific search.
Using the latent semantic indexing concept, a web page about gardening could also include related words such as plants, landscape, and growing. A wider variety of text can be used for keywords and phrases, rather than the same ones repeated throughout the site. The web page can be ranked based on its ability to match not only the specific search term, but also terms which are considered related.
See LSI in Action
Test latent semantic indexing by doing a search on Google using the word music. Note that in the results only the word music is bold. Do another search, but add a tilde in front of music by holding down the shift key and and the key to the left of the 1. The results from the second search will include words such as radio, records, and MP3 in bold, in addition to the word music.
Benefits of LSI
Search engines incorporating LSI can enable more extensive results by finding near matches to complicated searches where exact matches aren't found. SEO Book explains in greater detail how this is done through single variable decomposition such as removing common stop words and creating database of relevant keywords and all words in relevant documents. Another method involves an algorithm called multi dimensional scaling, which takes data and moves it around, calculating whether the projection is more or less accurate after each movement.
Since latent semantic indexing adds weight to related words in the content of websites, spam sites which are just filled with keywords and reciprocal links without additional information can actually be lowered in value. Additionally, web pages that are tightly focused on one keyword or phrase may rank worse in search engines using LSI. Inbound links to various synonyms and tenses of keywords in web pages and a wider variety of keywords and phrases throughout the content can work well for this more human seeming approach to ranking in search engines. Though still new, LSI might be an important factor to consider in order to reach and maintain a high rank in Google and other search engines.
Latent Semantic Indexing
Author: Patricia Brand
Published: October 18, 2007 Original Article can be found HereSuite101
Reps are nice
Latent Semantic Indexing
Overview of latent semantic indexing and how it relates to search engine optimization
Keyword density, search engine optimization, and reciprocal links have long been essential elements for More...getting websites ranked high in search engines. But with rising numbers of sites created for the sole purpose of targeting keywords without valuable content, new methods have became necessary to separate sites with relevant information from empty shells. Latent semantic indexing presents the idea of ranking sites by theme rather than keyword.
Ranking by Theme
By including related words, latent semantic indexing is meant to allow a more natural approach for search engines to view and rank websites. This changes the approach from focusing on keyword usage to looking for themes. In essence, LSI can be used to identify websites which contain information visitors will find relevant to their specific search.
Using the latent semantic indexing concept, a web page about gardening could also include related words such as plants, landscape, and growing. A wider variety of text can be used for keywords and phrases, rather than the same ones repeated throughout the site. The web page can be ranked based on its ability to match not only the specific search term, but also terms which are considered related.
See LSI in Action
Test latent semantic indexing by doing a search on Google using the word music. Note that in the results only the word music is bold. Do another search, but add a tilde in front of music by holding down the shift key and and the key to the left of the 1. The results from the second search will include words such as radio, records, and MP3 in bold, in addition to the word music.
Benefits of LSI
Search engines incorporating LSI can enable more extensive results by finding near matches to complicated searches where exact matches aren't found. SEO Book explains in greater detail how this is done through single variable decomposition such as removing common stop words and creating database of relevant keywords and all words in relevant documents. Another method involves an algorithm called multi dimensional scaling, which takes data and moves it around, calculating whether the projection is more or less accurate after each movement.
Since latent semantic indexing adds weight to related words in the content of websites, spam sites which are just filled with keywords and reciprocal links without additional information can actually be lowered in value. Additionally, web pages that are tightly focused on one keyword or phrase may rank worse in search engines using LSI. Inbound links to various synonyms and tenses of keywords in web pages and a wider variety of keywords and phrases throughout the content can work well for this more human seeming approach to ranking in search engines. Though still new, LSI might be an important factor to consider in order to reach and maintain a high rank in Google and other search engines.
Latent Semantic Indexing
Author: Patricia Brand
Published: October 18, 2007 Original Article can be found HereSuite101
Reps are nice
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