Search engine optimization or (SEO) has been the go-to marketing strategy for many businesses for almost three decades. Since the inception of the internet, the number of powerhouse search engines have drastically decreased with anyone familiar with the SEO industry telling you there are only three that matter for SEO. Google, Bing, and Yahoo are undoubtedly the largest search engines to date with Google accounting for more than 90% of all search traffic of the web, 92.92% to be exact.
Throughout the year, Google releases many updates to its core ranking algorithm which dictates the overall visibility of web pages controlling the amount of traffic each receives. Past updates have impacted search results based on the number of backlinks a web page has, it’s content length, keyword density, internal linking structure, and many more elements of effective SEO. One of these updates happened a few years ago when Google incorporated the mathematical phenomena known as the Latent Semantic Index retrieval method.
Latent Semantic Indexing or LSI has changed the world of search engine optimization. One day, many SEO experts awoke only to find that most of their best ranking sites on Google were in jeopardy and losing rankings fast! Google simply implemented another update to their algorithm denoting old-spammy strategies. Google’s shift to accommodate LSI has moved its algorithm to more relevant queries and an overall higher user experience. With Google owning most of the world’s search queries, it’s userbase is undoubtedly a vast portion of it’s $68 billion pay per click advertising platform known as Google Ads.
LSI is known as an indexation and retrieval method which identifies patterns and their relationships to other terms in an unstructured collection of text. LSI uses statistical probability and correlation that helps deduce semantic distance between words.
In other words, LSI a very complex algorithm.
Although LSI is very complex, it is not too difficult to implement properly once you begin to understand the relationship between words. In SEO, LSI understands the relationship between keywords in a paragraph or web page and serves the end user/ searcher results based on their query.
When you Google a question, nine times out of ten, if you scroll to the bottom of the search, Google will show you related searches in a table called “Searches related.” Sometimes these related searches will contain your actual search phrase or keyword, and sometimes they will be different.
LSI is applied while indexing the depth of the web adding to the search engines database. In Google’s case, the “searches related” field is propagated due to hundreds of thousands or millions of web pages containing content that sounds similar to what the user is searching for.
For instance, if one million articles mention the exact phrase “LSI keyword strategies” and in the same sentence or paragraph, 80% also mention “SEO,” Google’s algorithm will put two and two together and serve anyone looking for LSI Keyword Strategies with results relating to SEO.
In this example, if we google search “lsi keyword strategies,” then scroll to the bottom to “Search related” we’ll read “Searches related to LSI keyword strategies.”
In the same example, (either by coincidence or intuition) the first query is “lsi seo.”
To summarize, LSI not only focuses on studying web pages for keywords and accurately listing it in the database but also studying the collection of documents, recognizing and identify words and phrases that are common between those documents.
Concluding the semantic relation between words being used in web pages, this process also finds which other documents include or make use of these semantically close words. The results are then indexed and displayed as related or closely relevant.
When it comes to building an effective SEO strategy, LSI regards the documents with a certain proportion of words being used frequently to be semantically close. If there are fewer words common among documents, they are supposed to be semantically distant.
Therefore, Latent Semantic Indexing introduces interdependence of measure, and it rates the relevance of any document on a scale of 0 to 1.
Unlike regular keyword searches, LSI can acknowledge the measure of how close is a document to another or how relevant is a credential to a particular context.
Let’s consider an example here. In a document that discusses Stephen Covey and his preaching, words such as ‘effective’, ‘habits’, ‘interdependence’, ‘independence’, ‘synergic’, ‘paradigm’, ‘continuum’, ‘public victory’, ‘private victory’, ‘circle of influence’ and so on would be found frequently.
The search engine indexing tool that uses the LSI technique recognizes these commonly-used words from a given set of documents.
It then finds other documents or web pages online that contains the same set of keywords in almost similar frequency and index them in the database beside the relevant context (Stephen Covey and his preaching) that it leads to.
Now compare this simple method with a human brain’s approach to search a context. If you are given a set of documents and asked to locate the ones that discuss a particular context, what will you do? Anyone will try to find out the things in common in the sample context and use the observation to compare the rest of the documents to classify them. This concept has been added to artificial intelligence or computer technology through the LSI technology.
Quite obviously, the Latent Semantic Indexing algorithm doesn’t understand anything about the meaning or definition of a word on a web page. It just reads through the pattern of the usage of particular words and calculates the correlation of their occurrence and hence their correlation with a particular context.
The more practical side of LSI is how it is applied to a search engine strategy. I’ll be covering this in my next, next article featuring all the free tools I use for finding the best long-tail keywords, keyword density, and LSI generators.