LSIGraph (Latent Semantic Indexing) plays a big part in SEO
What is LSIGraph ?
It’s likely you’ve come across the term LSI loosely thrown around in the context of search engine optimization. LSI or Latent Semantic Indexing seems to be an increasingly popular topic of discussion in the context of SEO. Many companies, such as LSIGraph, prominently advertise on their webpages that, by simply paying for a set of “LSI keywords”, corresponding to your topic of choice will help. For example, it helps pick related keywords that you then and create content for your website. Thus, help your website rank higher and generate more traffic.
Such companies’ business model rides on the assumption that the introduction of these proposed “LSI keywords” to a website will somehow result in search engines ranking their websites higher. This theoretically would drive more clicks and consequently, more profit for the website owners. However, it seems few people actually 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, that adding their specific LSI keywords will 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, as it is alternatively popularly known, latent semantic analysis? In short, latent semantic indexing refers to a technique developed in the 80s that is used to map a short string of words such as a sentence, to an arbitrary dimensional (N-dimensional) real-valued vector with a unique desirable property. Sounds complex hey? Basically, its recognizing words that are related and have similar intentions. For example, special semantic relationships in the specific language are mapped to real-valued vectors which are 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 given this name as its values don’t correspond to anything real. Instead, they correspond to some abstract notion of meaning which is hidden or latent. Essentially, 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 strong semantic relationship in that the occurrence of one in a 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 under the latent semantic mapping, they would be assigned real-valued vectors. This is all fine and good but the question of how one actually constructs this mapping from term space to latent space arises
Constructing the Mapping via LSI
The first step is to obtain a large collection of text. For the purposes of 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 are then laid out that correspond to the individual websites and rows correspond to a specific subset of the terms present. Each matrix element is then populated with a real value corresponding to the frequency of the occurrence of the specific terms. Such a matrix is often referred to as the term-document matrix which is a special case of the occurrence 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 product of the magnitude of two column vectors. Generally, the results roughly 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.
The dot product of two document columns can be also divided by the product of the magnitude of two column vectors. It all sounds foreign if you’re not familiar with linear algebra? Basically, the dot product is just the sum of the products of each pair of values from each column array. 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 go much further through 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 that approximately preserves the properties of the term-document matrix, though has a lower dimensionality. Lower dimensionality will make 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, though it is most commonly performed through a technique from linear algebra called singular value decomposition.
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 diagonal matrix factor are referred to as singular values. Also, by removing the lowest singular values from the diagonal matrix and the corresponding rows and columns from the two rotation matrix factors, you can get a new lower-dimensional matrix that closely approximates the original matrix.
Applying this technique to the term-document occurrence matrix, you can get a transformation from 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! The transformation matrix process enables us to assess, quantitatively, the similarity of the meaning (semantic similarity) of two bodies text.
Simple Terms of LSI or Not…
It provides us with the capability to determine how two related bodies of content are. To determine the semantic relevance of two documents to each other, all that needs to be done is to convert the documents to 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 basically just taking the dot product of the input vector with each row of the matrix in question. Each of the values you get out becomes the elements of your new transformed vector.
The distance of the two document vectors in the latent space then 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. They will be close together. Alternatively, if the documents are unrelated, their distance in the latent space will be relatively high.
An Example Usage
If you’ve been following 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 through the use of a large collection of textual content. Then, through the singular value decomposition rank reduction technique, you could construct a transformation matrix from word frequency space to latent space.
When a client uses your hypothetical search engine to search for relevant websites, each websites’ distance from the search terms specified by the user in the latent space would be evaluated. The lower the distance in latent space, the higher the rank of the website 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 easily inflate our website’s ranking. Let’s say, for example, the website we’re attempting to inflate the ranking of contains information about bananas. When the user enters a search string containing terms related to “bananas”, we want them to see our information on the banana websites. To achieve this, we can simply look for alternative terms that are close in the latent space to 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, rank higher.
Companies such as LSIGraph make a business of producing their own transformation matrices through latent semantic analysis. They provide access to paying customers to their latent semantic indexing database. Customers simply enter the topic of their website which they are attempting to optimize and our provided with words that are closely semantically related. The semantic relationship is measured by the proximity of the produced terms in the latent space.
You can probably begin to guess that latent semantic indexing’s relation to search engine optimization? Theoretically one could construct a rudimentary search engine through the use of a large collection of websites’ textual content as the document columns. Then, through the singular value decomposition rank reduction technique, one could construct a transformation matrix from word frequency space to latent space. Then, when a client uses your hypothetical search engine to search for relevant websites, each websites’ distance from the search terms specified by the user in the latent space would be evaluated. The lower the distance in latent space, the higher the rank of the website in the returned results. Consequently, only websites with the most significant semantic relationship to the client’s search terms are presented.
Next, let’s consider how we might go about artificially increasing the ranking of a website as presented to the user by our hypothetical search engine. 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 easily inflate our website’s ranking. Let’s say, for example, the website we’re attempting to inflate the ranking of contains information about bananas. Naturally, in this hypothetical, when the user enters a search string containing terms related to bananas, we want them to see our informational banana website. To achieve this, we can simply look for alternative terms that are close in the latent space to 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, ranked higher on average.
In our example, we optimized a website for our latent semantic analysis based search engine. The assumption that injecting these proposed latent semantic keywords into websites will magically increase rankings in real-world browsers just as it did in our hypothetical is critical to the business models of companies such as LSIGraph.
The only issue is that there is little evidence to support that this search engine optimization technique works. After all, Google along with other search engines don’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, the state of the art in search engine technologies has advanced considerably. The exact algorithms utilized in Google’s search engine remain a secret, partially by design, it certainly uses more sophisticated techniques than LSI.
While modern search engines rely on semantic latent spaces, these latent space 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.
Also, using LSI can help with achieving more keyword variety but remains debatable. Web design agencies, as well as most SEO agencies, continue to find ways to help websites rank, but Google guidelines are specific. In fact, it will only become harder to game the ranking process, hence why content should be aimed at the visitors. Yes, there are other factors that help websites rank. For example, signals from the likes of social media feed. Overall, websites should be built with relevant content, structure, and user experience.
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