OntoSearch

Google’s Latent Semantic Indexing

Billions of sites used to saturate the net. The endless onslaught of spam, link farms, irrelevant ads and so forth surely didn’t aid the scenario, either. Hence, data overload took place-followed by what seemed like a search-engine event horizon.

Around that time, Google said “enough is enough” and began making use of the latent semantic indexing (LSI) method more and more over the legacy way of doing things-that is, using keywords and keyword strings to dish out relevant search results. LSI, as most insiders know, usually provides a lot more confident search results.

What Exactly is LSI?

LSI is a word relationship approach of generating relevant search results, based on natural language processing. Whilst it’s not the only technology that moguls like Google use, it’s becoming immensely well-known. LSI is a a lot more sophisticated method than using keywords/key phrases alone; it uses a complex set of algorithms that automatically compute statistical probabilities based on the frequency (semantic distance) of words or keyword phrases that are relative to the actual context of the pages they are embedded on and to other pages comparable to it.

Is it technical? Yes. To break it down a little, take into account this: the LSI search approach uses certain parameters to gauge the “distance” between words on a site and compares the difference to the overall theme of the web site and to that of other comparable sites that have the highest click quotient. The ultimate goal is to prune as many useless or irrelevant websites from the top outcomes of a search and only show the most reliable. Take into account this about Google:

• Google’s engineers continually refine their search parameters to weed-out or rank quite lowly spam-which consists of, yes, sites that are ripe with overstuffed keywords and phrases (a condition otherwise known as ‘over-optimized’).

• Pages with a variety of related keywords tend to rank higher with Google.

• LSI takes its ”findings” about the page and then proceeds to analyze, or scan, the remainder of the internet site-looking for extra relevant and related content.

• It’s very good to maintain in mind that while latent semantic indexing analyzes and computes statistical relationship among words, it understands nothing about the individual words or the context that they’re employed in.

It can be a little challenging to visualize the method, but once you realize, every thing essentially comes full circle.

Relevant sites are then rounded up and grouped by their overall theme. For instance, typing “2010 Mercedes-Benz S-Class” into Google ranks millions of pages nearly instantly on parameters such as the ones set forth above. Utilizing the overall themes of those pages, the program groups the most relevant internet sites with the words “Mercedes-Benz” “2010″ “S” “Class”, chooses lists the sites most closely relate to themes (as determined by LSI) such as “cars”, “automobile reviews”, and “luxury”-and displays the pages with the highest number of clicks that are relative to both the theme and the anchor text (keywords and phrases).

Implementing LSI and Google’s Semantically Related Words

There are a myriad of proven approaches to use in successfully making your site LSI-friendly:

• Try the Google AdSense sandbox approach on a block of sample text. This, basically, tells you how strong or weak the anchor text and related keywords are. • Learn to alter key phrases and words without changing the meaning; there are a ton of very good keyword suggestion applications out there. • Never center your site around 1 or two keywords; instead, base it off of a primary theme and expand into related sub-themes. • Keywords are still essential; use similar words to them, as well as plural forms of them and vary (where achievable) the tense of related verbs across the website. • Use inbound links wisely. While targeting the keyword(s) is important, focus similar and relevant variations of them around inbound links as well.