Latent Semantic Indexing Tutorial for Better Content

Latent Semantic Indexing

Development of better online content is important to the continued growth and breadth of the internet. Over the years, internet users have been relying on receiving the most popular content in terms of ranking whenever they made searches. However, this method of optimization allowed websites to concentrate hundreds of keywords within a page in a manner that simply filled random words with no worthwhile content pertaining to the subject matter being searched.

To Google’s popular search engine, and others, this amounts to cheating, which ultimately could endanger the quality of information obtained from online searches. As a matter of fact, this form of cheating can lead to punishment for culprit sites, which has made website creators and content writers become wary of over-optimizing content and keyword stuffing.

Semantic AnalysisLatent semantic indexing (LSI) was introduced as an effective alternative for optimization searches on Google due to its ability to give better rankings and provide a more sensible content search. It uses mathematical techniques where patterns are recorded to identify the specific relationships between the keywords within a collection of texts. Analysis of these patterns reveals that similar words in meaning are found within the same pieces of online texts.

For example if a user searches the word cloth stains, Google utilizes LSI to give the specific and relevant results such as: how to remove stains, types of stains, among others. This optimization of content helps the user to pick the most relevant search among the ones listed.


In this leveraging context, a semantic search is able to receive relevant data since all texts in the search are based on quality material. Google’s objective is to provide readers with good content and at the same time ensure that searches return the best rankings.

To achieve this, a Google Knowledge Graph also comes in handy. Using this knowledge base, Google can easily group similar relationships together meaning that in every search it recognizes the text as an entity. There are detailed codes of information that are interlinked to other sites to ensure that online users can easily access this information, without navigating to each of these sites.

With the consolidation of similar search sites, Google has also introduced Hummingbird, which is a new engine that empowers Google to anticipate user’s search needs. This new technology considers every word in the user’s query, which ensures that the whole search sentence is included in the search, rather than few key words. It leads to online content that is most relevant and meaningful to the subject matter. These optimization tools are are directed towards boosting searches to give online users the most relevant information.