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Latent Semantic Indexing - A Simple Approach Have you ever thought to yourself, "What in the world is Latent Semantic Indexing"? I have, and so I decided to find out what Latent Semantic Indexing (or LSI) is all about. This term is confusing to many, but I will tell you what I learned about the LSI, and how LSI performs a search on words in a document or article for the semantic meaning. There are terms involved in Latent Semantic Indexing such as metric, indexing, SEO, blah blah blah. I found a lot of confusing ideas in Latent Semantic Indexing, but I will try to sort out what I learned about LSI. With the advance of technology and the rise of internet marketing, use of a website or article to give info or to make a sale, Latent Semantic Indexing has come to the rescue of Google. The question it answered is, "How in the world do you provide the correct information with any given search or query?" Latent Semantic Indexing, through a complex program, looks at a search, does analysis on the words and article or articles, performs indexing on the documents and through a complicated matrix does more analysis and returns the results. It ranks the website or articles in the result depending on the analysis. Latent Semantic Indexing came into being from the problem of how to find the relevant documents for a particular search of words. What words to analyze? Which documents should result from a search from an article? What part does internet marketing play? When looking at a document or article, what are the important words? Latent Semantic Indexing arose from these questions. Google uses Latent Semantic Indexing to sort through the endless documents, terms, articles, and every website available. LSI to the rescue! I will explain Latent Semantic Indexing as I understand it. Latent Semantic Indexing, or LSI, means to analyze a document to find the underlying, or "latent" meaning. In the term Latent Semantic Indexing, or LSI, "semantic" is the meaning of words. Latent Semantic Indexing decides if documents have many words in common. If they do they are considered to be close, in semantic terms. If documents have fewer words in common, they are considered to be distant in semantic terms. This grouping of words and search of the entire document to analyze words and terms is the key to Latent Semantic Indexing. Latent Semantic Indexing can even return results that don't have the keyword in at all. I will skip the complicated part of Latent Semantic Indexing or LSI and leave the semantic and latent search of documents, matrix, indexing terms, SEO (search engine optimization) and query and search of Google articles for analysis up to the experts. The bottom line in Latent Semantic Indexing is that, for now, it works. It finds relevant documents when I do a search on Google and frankly, that's all I care about. If a simple explanation of Latent Semantic Indexing is what you are after, I hope this article on LSI has been useful. Latent Semantic Indexing or LSI compares what we search for with endless documents or articles on the website and provides relevant results. That is what I learned about Latent Semantic Indexing - hope it helps! 2355415c84c6048b2502afaee3d90f95 http://LSIKeyWords.com http://lsikeywords.com/Latent-Semantic-Indexing.html 2355415c84c6048b2502afaee3d90f95
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