Search Engine Optimization (SEO) Firm India LSI Based SEO Company

Wild net implements LSI by computing SVD or Singular Value Decomposition with the use of large matrix methods such as Lanczos methods. This allows us to diagnose the complexities of a matrix and provide numerical answers, thereby fine tuning our key word research. Furthermore, the use of a Vectorial semantic space model when computing a document-term matrix permits us to represent the subject of a document by the frequency of semantically significant terms. This is an efficient way to compute and store the word counts.

The words are then arranged in a sparse matrix whose rows correspond to documents and columns to phrases, words or terms. This matrix determines the relationship between terms and concepts and between the same concepts and different documents. This indirectly links terms and different concepts. Any efficient data retrieval system must overcome the basic difficulty wherein the presence or absence of a word is insufficient to establish relevance. At Wildnet we study the indexing process and are able to determine the appropriate phrases for a topic. The use of tilde in the search process using a search engine throws up many words which are semantically close to the key phrase.

Our Delhi based SEO firm Wildnet Technologies uses LSI based SEO in all methodologies to huge advantage. We use it extensively in our key phrase research, anchor text selection and optimization and other services. In standard key phrase searches it is seen that search results are reduced with the increase in the number of keywords that are used. With the use of LSI the opposite happens. The search engine focuses on understanding and analyzing the document before indexing or categorizing it. With a conventional keyword search a document either contains the given word or it does not. There is no via media. Each document is judged on its singular content or merit; there is no interdependence of any kind between documents.

We incorporate LSI in link building and meta tags etc. LSI uses relevance feedback wherein the initial results returned are studied for relevance with the topic and other documents to perform further query. The user can thus perform a repeated search with increased sensitivity. We take advantage of the fact that LSI will provide useful feedback to frame a better search and more relevant data retrieval. LSI can retrieve any part of the content that is relevant even though the entire document may not cover the same topic. It compares data or content on a given topic and with its automated grading system compares the content with the sample. LSI is more archive friendly, permitting users to index and label the categories effectively.  Every document need not be indexed from scratch.

LSI attempts to take care of the problems of synonymy, polysemy and noise that occur with normal keyword searches. Different words can be used to describe the same idea and the same word can have multiple meanings. Two documents may share words but may be dissimilar in content or may not use common terms yet be close in content. Synonymy results in false negatives and polysemy in false positives. At Wildnet by constructing small matrices we overcome this problem with LSI. In conventional key word searches singular and plural form of a word of different parts of speech derived from the same root word are not picked up, whereas with the use of LSI our Delhi based LSI SEO Company ensures that they are.

The process of LSI in fact is a boon to our talented content writers who can focus on giving our clients high quality content with the confidence that it will be ranked high for its relevance and information. The process uses the features of natural language whereby words with similar meaning are considered together and unnecessary noise and information is cut out. While carrying out Latent Semantic Indexing, common words such as articles, (like a, an, and the) conjunctions (such as and, but, if) and prepositions (on, in, at, by, etc) are ignored. So are common verbs such as (is, has, are, have) and adjectives like (big, high, tall). The unnecessary words being eliminated, the document is left with words with some semantic meaning.