Thread: What is LSI indexing

What is LSI indexing?Give me some detail about it

Latent Semantic Indexing is a way for search systems to mathematically understanding and representing language based on the similarity of pages and keyword.

LSI is short from of the Langelier Saturation Index or Langelier Stability Index. LSI is a calculated number used to predict the calcium carbonate stability of water; that is, whether a water will precipitate, dissolve, or be in equilibrium with calcium carbonate. LSI considers documents that have many words in common to be semantically close, and ones with few words in common to be semantically distant. LSI is an attempt to overcome this problem by looking at patterns of word distribution across the whole of the web.

This methodology is used while indexing a page in the search engine's database. LSI is involving statistical probability and correlation that helps deducing the semantic distance between words. It's obviously a complex methodology but can be easily applied to understand the relation between certain words in a paragraph or in a document.

Hello,

LATENT SEMANTIC INDEXING

Taking a Holistic View
Regular keyword searches approach a document collection with a kind of accountant mentality: a document contains a given word or it doesn't, with no middle ground. We create a result set by looking through each document in turn for certain keywords and phrases, tossing aside any documents that don't contain them, and ordering the rest based on some ranking system. Each document stands alone in judgement before the search algorithm - there is no interdependence of any kind between documents, which are evaluated solely on their contents.
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. LSI considers documents that have many words in common to be semantically close, and ones with few words in common to be semantically distant. This simple method correlates surprisingly well with how a human being, looking at content, might classify a document collection. Although the LSI algorithm doesn't understand anything about what the words mean, the patterns it notices can make it seem astonishingly intelligent.
When you search an LSI-indexed database, the search engine looks at similarity values it has calculated for every content word, and returns the documents that it thinks best fit the query. Because two documents may be semantically very close even if they do not share a particular keyword, LSI does not require an exact match to return useful results. Where a plain keyword search will fail if there is no exact match, LSI will often return relevant documents that don't contain the keyword at all.
To use an earlier example, let's say we use LSI to index our collection of mathematical articles. If the words n-dimensional, manifold and topology appear together in enough articles, the search algorithm will notice that the three terms are semantically close. A search for n-dimensional manifolds will therefore return a set of articles containing that phrase (the same result we would get with a regular search), but also articles that contain just the word topology. The search engine understands nothing about mathematics, but examining a sufficient number of documents teaches it that the three terms are related. It then uses that information to provide an expanded set of results with better recall than a plain keyword search


Thanks

Hello folks, Latent Semantic Indexing is a way for search systems to mathematically understanding and representing language based on the similarity of pages and keywords.

Latent Semantic Indexing is a way for search systems to mathematically understanding and representing language based on the similarity.

Posting on wrong thread category you should post in seo categorey on this forum.

Hi..

LSI(Latent Semantic indexing) is the term used in Search Engine Optimization. Here as the name indicates search engine will make database of the words that have semantically close and the one with semantically distinct. Even though plain keyword searching fails, when there is no exact match, it can return the relevant document.It is the ability to extract contents of body text using the principle of semantic matching.

LSI is an indexing and retrieval method that uses a algebraic address alleged Singular Value Decomposition to identify patterns in the relationships between the terms and concepts independent in an baggy accumulating of text. LSI is an attempt to overcome this problem by looking at patterns of word distribution across the whole of the web.

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. LSI considers documents that have many words in common to be semantically close, and ones with few words in common to be semantically distant.