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LSIGraph (Latent Semantic Indexing) plays a big part in SEO

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What is LSIGraph?

You may have encountered the term LSI, especially regarding search engine optimization. LSI, or Latent Semantic Indexing, seems to be an increasingly popular topic of discussion in SEO. Many companies, such as LSIGraph, advertise on their web pages that pay for a set of “LSI keywords” corresponding to your topic of choice to help a website rank. For example, such tools help you pick related keywords for which you create content. This, in turn, helps your website to rank higher and generate more traffic.

Some businesses rely on the assumption that introducing these proposed “LSI keywords” to a new website will somehow result in search engines ranking their websites higher. This theoretically would drive more clicks and, consequently and potential customers. However, it seems few people understand latent semantic indexing.

So, what is this mysterious latent semantic indexing that everyone seems to be talking about in the context of SEO? Is there any validity to the claims made by various LSI keyword companies, such as LSIGraph? For example, does adding specific LSI keywords increase clicks and cause search engines to rank your website higher?

LSIGraph – Latent Semantic Indexing

Latent Semantic Indexing

So what is latent semantic indexing or latent semantic analysis? In short, latent semantic indexing refers to a technique developed in the 80s to map a short string of words, such as a sentence, to an arbitrary dimensional (N-dimensional) real-valued vector. In turn, this resulted in a unique, desirable property. Sounds complex, hey? It recognizes words that are related and have similar intentions. For example, unique semantic relationships in the specific language are mapped to real-valued vectors relatively close. This arbitrary dimensional, real-valued space is called the latent space or “hidden” space and is the origin of the term “latent” in latent semantic indexing.

The N-dimensional Space

This N-dimensional space is named as its values don’t correspond to anything real. Instead, they correspond to some abstract notion of hidden or latent meaning. A similar meaning or close semantic relationship results in proximity in the latent space.

For example, consider the terms “happy” and “joyful.”

Observe that both terms are approximate synonyms of each other. Consequently, they have a robust semantic relationship. In particular, one in the body of the text is correlated in some significant way with the existence of the other. From this notion of semantic relation, you can reasonably assume that they would be assigned real-valued vectors under the latent semantic mapping. This is all fine, but how does one construct this mapping from term space to latent space?

Constructing the Mapping via LSI

The first step is to obtain an extensive collection of text. For our specific interest in SEO, let’s say the text corresponds to content on various websites. The next step is to construct a matrix from this content. Columns that correspond to the individual websites are then laid out, and rows correspond to a specific subset of the terms. Subsequently, each matrix element is populated with an accurate value corresponding to the frequency of the occurrence of that term. Such a matrix is often referred to as the term-document matrix, a particular case of the occurrence matrix. 

source: wikipedia.com

Matrix

This image shows an example of a small term-document occurrence matrix constructed from a small collection of documents—the dot product of two document columns divided by the magnitude of two column vectors. Generally, the results correspond to the semantic similarity of the two documents in word frequency space. Furthermore, this measure can be interpreted as evaluating the similarity of the direction of the two vectors in word frequency space. 

Term-Document Matrix

I bet it all sounds foreign if you’re unfamiliar with linear algebra. The dot product is the sum of the effects of each pair of values from each column array. The dot product of two document columns can also be divided by the magnitude of two column vectors. This roughly corresponds to the similarity of the two documents in word frequency.

For example, similar documents mean more dots. Furthermore, this measure can be interpreted as evaluating the similarity of the direction of the two vectors in word frequency space. We can also go much further by conceptually decoupling the model’s understanding of semantic equivalence from word frequency.

The rank of the term-document matrix

The next step is to reduce the rank of the term-document matrix to produce a new matrix. Though it has a lower dimensionality, it preserves the properties of the term-document matrix. Lower dimensionality makes it easier for computers by making the matrix smaller. Otherwise, the matrix would be too large for practical use. This semantic distance conserving approximate rank reduction can be accomplished through various techniques. But, it is most commonly performed through singular value decomposition.

Smaller Matrix

Singular value decomposition is a technique for creating two rotation matrices and a single diagonal matrix. The values of the matrix elements along the main diagonal of the oblique matrix factor are referred to as singular values. You can get a new lower-dimensional matrix by removing the lowest singular values from the diagonal matrix and the corresponding rows and columns from the two rotation matrix factors. The result closely approximates the original matrix.

The transformation matrix process enables us to quantitatively assess the similarity of the meaning (semantic similarity) of two bodies of text. Applying this technique to the term-document occurrence matrix can transform from a term frequency space to a latent space. This approximate rank reduction process has the additional benefit of denoising the matrix. Also, these techniques are effectively the essence of latent semantic indexing. If you’ve followed along, you now understand LSI!

Simple Terms of LSI or Not…

It provides us with the capability to determine how two related content are. For example, to determine the semantic relevance of two documents to each other, they need to convert into two-word frequency column vectors. These vectors can then be individually transformed into the latent space by multiplying them by the transformation matrix. Matrix multiplication consists of just taking the input vector’s dot product with each row of the matrix in question. Hence, each value you get out becomes the elements of your new transformed vector.

The distance of the two document vectors in the latent space gives us a fancy way to measure the semantic relationship between the two documents. If the documents are more closely related, their distance in the latent space will be relatively low. Alternatively, if the documents are unrelated, their distance in the latent space will be pretty high.

An Example Usage

If you’ve followed along so far, you can probably begin to guess about latent semantic indexing’s relation to search engine optimization. Theoretically, one could construct a rudimentary search engine using an extensive textual content collection. Then, through the singular value decomposition rank reduction technique, you could build a transformation matrix from word frequency space to latent space. 

When a client uses a search engine to search for relevant websites, each website’s distance from the search terms specified by the user in the latent space is evaluated. The lower the distance in latent space, the higher the website’s rank in the returned results. Consequently, only websites with the most significant semantic relationship to the client’s search terms are presented.

Artificial Intelligence and LSI

Armed with the knowledge that our search engine presents websites to the user based on their distance in the latent space. We can quickly inflate our website’s ranking. Let’s say, for example, the website we’re attempting to increase the ranking of contains information about bananas. When the user enters a search string containing “bananas” terms, we want them to see our knowledge based on banana websites. We can look for alternative terms close in the latent space to bananas and inject them into our website to achieve this. Theoretically, this will result in our website being closer in the latent space to a wider variety of search terms related to bananas and, consequently, ranking higher.

LSIGraph

Companies such as LSIGraph produce their transformation matrices through latent semantic analysis. They provide access to paying customers to their latent semantic indexing database. Customers enter the topic of their website, which they are attempting to optimize, and are equipped with closely semantically related words.

Artificially Inflate Website Ranking

Let’s consider how we might go about artificially increasing the ranking of a website. Armed with the knowledge that our search engine presents websites to the user based on their distance in the latent space from the user’s search terms, we can quickly inflate our website’s ranking. For example, the website we’re attempting to inflate the ranking contains information about bananas. When the user enters a search string containing terms related to bananas, we want to ensure synonyms and related content to bananas include alternative terms close in the latent space bananas and inject them into our website.

Theoretically, this will result in our website being closer in the latent space to a wider variety of search terms related to bananas and, consequently, ranking higher.

Bottom Line

The assumption that injecting these proposed latent semantic keywords into websites will magically increase rankings is invalid. Many other factors play a part in ranking a website! Remember, as online marketers.

LSI Generator

The only issue is that there is little evidence to support that this search engine optimization technique works based on this single strategy. After all, Google, along with other search engines, doesn’t release the algorithms so that SEO companies and online marketers don’t start gaming the ranking process! 

While early search engines relied on latent semantic analysis, state of the art in search engine technologies have advanced considerably. The exact algorithms utilized in Google’s search engine remain a secret, partially by design. It indeed uses more sophisticated techniques than LSI.

Modern search engines rely on latent semantic spaces. These latent spaces are likely produced through modern machine-learning techniques such as autoencoders. Organic content is much more critical to achieving a high ranking than meeting some keyword checklist.

Web design agencies and most SEO agencies continue to find ways to help websites rank, but Google guidelines are specific. It will only become harder to game the ranking process, so content should be aimed at the visitors. Yes, other factors help websites rank—for example, signals from the likes of social media feed. Also, using LSI can help achieve more keyword variety, but it remains debatable. Overall, websites should have relevant content, structure, and user experience. 

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