OntoSearch

“LSI fits in wonderfully and enhances the search engine’s power to converge with artificial intelligence…”

Now, what do we gain by way of the use of this LSI technology?

To answer briefly, we can say that with LSI, search engines took a step forward to give us an perfect search result. Now you would ask – what is an ideal search result? Then, answer this! What do you look for when you sort in a keyword or a context in Google’s search text box?

With the number of Web pages increasing voluminously on the Web, we would like to rely doubtlessly on the search engine and want to use them as a librarian with massive capacity to recall, capacity to give the most precise and relevant outcomes and that too, with fantastic sense of ordering. More technically, the perfect search engine need to be able to cater to this trinity of recall, precision, and order. And this is where LSI fits in wonderfully and enhances the search engine’s power to converge with artificial intelligence.

Let’s have a look at the dumb computer difficulties that LSI can well take care of.

As we said earlier, a conventional search engine based on keyword searching may not give you the very best results. This is just due to the fact the search engine programs can not differentiate between:

* Similar words with distinct meanings, ex: Monitor workflow or monitor

* Words that are comparable in meaning but spelled differently, ex: illness and maladies

* Singular and plural forms of words, ex: button and buttons

* Words with similar roots, such as differed, differs, and different

* Other grammatically diverse words, such as roast, roasted

Improved SERPs

The LSI, because it focuses on a bunch of keywords, so to say, and not a single keyword,

and by way of its studied pattern of the relationship between semantically close and distant words in a collection of documents, it do not get confused between singular and plurals, or synonyms. It simply goes on to come across the context developed by a bunch of keywords. So that, when you search for Tiger Woods, it doesn’t go on to look for Web pages that has utilized the keywords “tiger’ and ‘woods’ but lists a collection of pages that discusses Golf. This is what is called relevance feedback .

Usually, you will find that your search results are reduced with the boost in the number of keywords you search for. This is simply because a search engines functions much better when they study, index, and recall for shorter and a simpler set of keywords. LSI goes the other way round and initial focuses on understanding and analyzing a document exhaustively just before indexing or categorizing it. Consequently, a latent semantic search engine makes it possible for a user to do an iterative search and supplies useful feedback to frame a greater search, if needed.

LSI is much more close to human-generated taxonomies and categorization and takes a lengthy step in structuring unstructured data. Hence, it is much more archive-friendly . It permits archivists to efficiently label and index the LSI-generated categories. LSI does half the job and each and every document will need not be indexed from scratch.

LSI helps in pointing out any part of content that is relevant but not covered in a document by comparing the data or content words on a given topic. This can find use in a number of contexts, one of them being a kind of automated grading system, where an assignment is compared to a sample of given quality.

LSI can investigate the semantic relationships within a text to determine on the relevance and consistency in the component parts. Adopting this into an application would enhance readability and comprehension. Naturally, these properties can be used effectively in instructional design and techniques

Stop SPAMS

However, the final and more relevant use of LSI is perhaps its power to filter details and stop spamming or distribution of unsolicited electronic mail. By adapting and adjusting a latent semantic algorithm on your mailbox and feeding the details of known spam messages into it, junk mail can be prevented more effectively than with the current

system of keyword based approaches.

LSI is an extremely methodical method that needs high amount of monotonous precision, 1 that a computer can do efficiently. As obvious, the technique involves a purely mechanical search based on an extensive evaluation of a set of words and comparing their presence in a significantly larger set of documents. The process can be automated simply because the computer does not need to recognize either the search query or the meaning of the words.