Search engines are developing machine learning algorithms to get the exact intent of human behavior to serve them more advanced results every time. Suppose if you type in "Safari Trip" than you will even receive "Zoo Trip" results within SERP.
in some circles, they say LSI is a myth spread by the SEO community to make money. Not sure if that's true, but it's been said.
Latent Semantic Indexing is not a science, it is simple common sense. Here are some simple guidelines: If your page title is Learn to Play Tennis, make sure your article is about tennis. Do not overuse your keywords in the content. It could look like keyword stuffing and the search engines may red flag you. Never use Article Spinning Software – it spits out unreadable garble. If you outsource your content, choose a quality source. Check Google Webmaster Tools and see what keywords your pages are ranking for.
LSI or Latent Semantic Indexing is for using phrases similar to the keywords in order to come up with better search results.
Latent semantic indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method examines the document collection as a whole, to see which other documents contain some of those same words.
Hey John Moris, I knwo my question is good, but it will be more good if you answer with some reason or explanation so that every one here can get help..
Latent Semantic Indexing is not rocket science, it is simple common sense. Here are some simple guidelines: If your page title is Learn to Play Tennis, make sure your article is about tennis. Do not overuse your keywords in the content. It could look like keyword stuffing and the search engines may red flag you. Never use Article Spinning Software – it spits out unreadable garble. If you outsource your content, choose a quality source. Check Google Webmaster Tools and see what keywords your pages are ranking for. Latent Semantic Indexing is not a trick. You should bear it in mind when adding content to a web page, but do not get paranoid about it. The chances are if you provide quality, relevant content you will never have to worry about falling foul of and LSI checks.
LSI in general form refers to using synonym of a word like car its LSI for can be automobile, automotive and so on. This helps in preventing keyword stuffing by repeating same word in content. Hope this helps yo understand more clearly!
Latent Semantic Indexing the best strategy to promote your post instead using the same words in your content and it's rank better too
LSI stands for Latent Semantic Indexing. “Semantics” relates to the meaning of words. What exactly LSI does is that it attempts to find the concepts associated with web pages by analyzing how words work in combination with other words. Depending on the context of the document there are terms, which have completely different meanings. LSI may change SERPs.
Understanding LSI LSI, or latent semantic indexing, is an algorithmic process that checks expected levels of word association. This is done through the process of semantic analysis. The study of semantics can be defined as the study of meaning and context in communication. The majority of interest in LSI comes from search engine optimization (SEO) specialists and webmasters attempting to rank their sites higher through on page optimization. SEO specialists naturally attempt to leverage knowledge and theory of how search algorithms work, in order to maximize their competitive edge. A factor that is rarely discussed, by these parties, is the way in which the algorithm also detects LSI spamming techniques. To really understand the process, it helps to have a working knowledge of semantic theory. How semantics works The process of natural language and writing is entirely built around the way in which we present our ideas. In speaking, a word is "colored" by its relationship to other words and the importance of that word within the overall context of what is being said. In topical writing the title can be considered the "charge" a core idea around which the content can be expected to revolve. Each phrase that is spoken adds clarification to the overall idea. The ideal number of related terms is based on statistical analysis of a vast number of documents within the indexed document pool. The usage of a periodically updated historical index in comparison to a live page creates an evolving basis and self-correcting tool for the search engine to work with. It is reasonable to expect that terms which return a lot of pages have a larger pool to work from and are going to have more stable and accurate numbers. Searches with lower return rates have less information to work with and will create a more mobile target An paraphrased example from the patent; "the word President is frequently associated with Bush or Clinton". This process helps to stack documents with similar documents for relevancy. This is useful for establishing search engine relevance and for delivering targeted advertisements. Related phrases In terms of algorithmic semantics there is an ideal rate of co-occurring related phrases, each loaded with meaning based on the words around it. Widely used keyphrases have a better data set to work with and will provide better statistical accuracy. This process serves two purposes: 1. If the correlation between the keyword and the related phrases is too low, then the document is probably best placed in a different category. For instance, if White House did not have the word Obama associated with it and instead was in proximity to words such as realtor, sales and sidewalk, the site would be placed in a different category altogether. At this point the search engine would probably look for other cues, within the site, to determine if another keyword entirely was the focus of the page. 2. If the correlation of related keywords is significantly higher than expected, the engine will have a very high possibility of triggering a spam scoring penalty. The algorithm uses this methodology to look for what is essentially keyword stuffing. This is possibly why many attempts to use LSI techniques do not perform as well as expected. Link weight An interesting factor in this entire process is that each keyword will have a unique expectation of variance. This makes almost any attempt at LSI very difficult to engineer. You cannot simply say, for instance, that the target is a .07 keyword density with a .13 related keyphrase density. Presumably a decent study could be made by evaluating top search results for a specific phrase and then removing link weight factors. A controlled study might have success in producing a decent estimate, unfortunately that estimate will likely vary with the target keyword. I suspect that it is possible to establish some rules of thumb to describe ranges based on the number of associated keywords within specific categories defined by the keyword planning tool. Relevant keywords Ultimately it is best to write naturally about the topic. If the article is relevant to the post title, the tendency will be to instinctively use the right number of relevant words. Structured writing should reinforce any titles in the subheadings. Well organized structure and a logical flow of discussion will do more for LSI keyword success than an overly conscious attempt to implement an LSI strategy.
Let's explain this with an example. Let's Say you have written an article based on the topic "Apple". NOw how google will understand that if the word "Apple" word belongs to Fruit or it belongs to Apple Gadgets. Here comes the concept of LSI - Latent semantic indexing. This concept says - if various other synonym words used like fruit, mango, orange then Google will take the "Apple" belonging to fruit. On the other hand if the synonyms used include - gadget, tablet, computers, iphone the the "Apple" will be taken as Apple Gadgets. Thanks
Everybody wants to get visibility in search engine results through targeted keywords so LSI is the tactics in which the content is created with the use of all the possible words related to your keyword to achieve more search possibilities.
LSI stands for Latent Semantic Indexing for more information please check in http://en.wikipedia.org/wiki/Latent_Semantic_Indexing
Latent Semantic Indexing (LSI).The contents of a webpage are crawled by a search engine and the most common words and phrases are collated and identified as the keywords for the page. LSI looks for synonyms related to the title of your page. For example, if the title of your page was “Classic Cars”, the search engine would expect to find words..