{"id":5713,"date":"2022-01-23T17:33:05","date_gmt":"2022-01-23T17:33:05","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2022\/01\/23\/is-it-a-google-ranking-factor\/"},"modified":"2022-01-23T17:33:05","modified_gmt":"2022-01-23T17:33:05","slug":"is-it-a-google-ranking-factor","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2022\/01\/23\/is-it-a-google-ranking-factor\/","title":{"rendered":"Is It A Google Ranking Factor?"},"content":{"rendered":"<p> <a href=\"https:\/\/go.fiverr.com\/visit\/?bta=1052423&nci=17043\" Target=\"_Top\"><img loading=\"lazy\" decoding=\"async\" border=\"0\" src=\"https:\/\/mailinvest.blog\/wp-content\/themes\/breek\/assets\/images\/transparent.gif\" data-lazy=\"true\" data-src=\"https:\/\/fiverr.ck-cdn.com\/tn\/serve\/?cid=40081059\"  width=\"601\" height=\"201\"><\/a>\n<\/p>\n<div id=\"narrow-cont\">\n<p>Latent semantic indexing (LSI) is an indexing and information retrieval method used to identify patterns in the relationships between terms and concepts.<\/p>\n<p>With LSI, a mathematical technique is used to find <strong>semantically<\/strong> related terms within a collection of text (an <strong>index<\/strong>) where those relationships might otherwise be hidden (or <strong>latent<\/strong>).<\/p>\n<p>And in that context, this sounds like it could be super important for SEO.<\/p>\n<p>Right?<\/p>\n<p>After all, Google is a massive index of information, and we\u2019re hearing all kinds of things about <a href=\"https:\/\/www.searchenginejournal.com\/semantic-search-seo\/264037\/\">semantic search<\/a> and the <a href=\"https:\/\/www.searchenginejournal.com\/seo-guide\/search-authority\/\">importance of relevance<\/a> in the search ranking algorithm.<\/p>\n<p>If you\u2019ve heard rumblings about latent semantic indexing in SEO or been advised to use LSI keywords, you aren\u2019t alone.<\/p>\n<p>But will LSI actually help improve your search rankings? Let\u2019s take a look.<\/p>\n<h2>The Claim: Latent Semantic Indexing As A Ranking Factor<\/h2>\n<p>The claim is simple: Optimizing web content using LSI keywords helps Google better understand it and you\u2019ll be rewarded with higher rankings.<\/p>\n<p>Backlinko defines LSI keywords in this way:<\/p>\n<blockquote>\n<p>\u201cLSI (Latent Semantic Indexing) Keywords are conceptually related terms that search engines use to deeply understand content on a webpage.\u201d<\/p>\n<\/blockquote>\n<p>By using contextually related terms, you can deepen Google\u2019s understanding of your content. Or so the story goes.<\/p>\n<p>That resource goes on to make some pretty compelling arguments for LSI keywords:<\/p>\n<ul>\n<li aria-level=\"1\">\u201c<strong>Google relies on LSI keywords to understand conten<\/strong>t at such a deep level.\u201d<\/li>\n<li aria-level=\"1\">\u201c<strong>LSI Keywords are NOT synonyms.<\/strong> Instead, they\u2019re terms that are closely tied to your target keyword.\u201d<\/li>\n<li aria-level=\"1\">\u201c<strong>Google doesn\u2019t ONLY bold terms that exactly match<\/strong> what you just searched for (in search results). They also bold words and phrases that are similar. Needless to say, these are LSI keywords that you want to sprinkle into your content.\u201d<\/li>\n<\/ul>\n<p>Does this practice of \u201csprinkling\u201d terms closely related to your target keyword help improve your rankings via LSI?<\/p>\n<h2>The Evidence For LSI As A Ranking Factor<\/h2>\n<p>Relevance is identified as one of five key factors that help Google determine which result is the best answer for any given query.<\/p>\n<p>As Google explains is its <a href=\"https:\/\/www.google.com\/search\/howsearchworks\/algorithms\/\" target=\"_blank\" rel=\"noopener\">How Search Works<\/a> resource:<\/p>\n<blockquote>\n<p>\u201cTo return relevant results for your query, we first need to establish what information you\u2019re looking for\u30fcthe intent behind your query.\u201d<\/p>\n<\/blockquote>\n<p>Once intent has been established:<\/p>\n<blockquote>\n<p>\u201c\u2026algorithms analyze the content of webpages to assess whether the page contains information that might be relevant to what you are looking for.\u201d<\/p>\n<\/blockquote>\n<p>Google goes on to explain that the \u201cmost basic signal\u201d of relevance is that the keywords used in the search query appear on the page. That makes sense \u2013 if you aren\u2019t using the keywords the searcher is looking for, how could Google tell you\u2019re the best answer?<\/p>\n<p>Now, this is where some believe LSI comes into play.<\/p>\n<p>If using keywords is a signal of relevance, using <strong>just the right keywords<\/strong> must be a stronger signal.<\/p>\n<p>There are purpose-build tools dedicated to helping you find these LSI keywords, and believers in this tactic recommend using all kinds of other keyword research tactics to identify them, as well.<\/p>\n<h2>The Evidence Against LSI As A Ranking Factor<\/h2>\n<p>Google\u2019s John Mueller has been <a href=\"https:\/\/www.searchenginejournal.com\/google-on-choosing-the-best-anchor-text\/398170\/\">crystal clear<\/a> on this one:<\/p>\n<blockquote>\n<p>\u201c\u2026we have no concept of LSI keywords. So that\u2019s something you can completely ignore.\u201d<\/p>\n<\/blockquote>\n<p>There\u2019s a healthy skepticism in SEO that Google may say things to lead us astray in order to protect the integrity of the algorithm. So let\u2019s dig in here.<\/p>\n<p>First, it\u2019s important to understand what LSI is and where it came from.<\/p>\n<p>Latent semantic structure emerged as a methodology for retrieving textual objects from files stored in a computer system in the late 1980s. As such, it\u2019s an example of one of the earlier information retrieval (IR) concepts available to programmers.<\/p>\n<p>As computer storage capacity improved and electronically available sets of data grew in size, it became more difficult to locate exactly what one was looking for in that collection.<\/p>\n<p>Researchers described the problem they were trying to solve in a <a href=\"https:\/\/patft.uspto.gov\/netacgi\/nph-Parser?Sect1=PTO1&amp;Sect2=HITOFF&amp;d=PALL&amp;p=1&amp;u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&amp;r=1&amp;f=G&amp;l=50&amp;s1=4839853.PN.&amp;OS=PN\/4839853&amp;RS=PN\/4839853\" target=\"_blank\" rel=\"noopener\">patent application<\/a> filed September 15, 1988:<\/p>\n<p>\u201cMost systems still require a user or provider of information to specify explicit relationships and links between data objects or text objects, thereby making the systems tedious to use or to apply to large, heterogeneous computer information files whose content may be unfamiliar to the user.\u201d<\/p>\n<p>Keyword matching was being used in IR at the time, but its limitations were evident long before Google came along.<\/p>\n<p>Too often, the words a person used to search for the information they sought were not exact matches for the words used in the indexed information.<\/p>\n<p>There are two reasons for this:<\/p>\n<ul>\n<li aria-level=\"1\"><strong>Synonymy<\/strong>: the diverse range of words used to describe a single object or idea results in relevant results being missed.<\/li>\n<li aria-level=\"1\"><strong>Polysemy<\/strong>: the different meanings of a single word results in irrelevant results being retrieved.<\/li>\n<\/ul>\n<p>These are still issues today, and you can imagine what a massive headache it is for Google.<\/p>\n<p>However, the methodologies and technology Google uses to solve for relevance long ago moved on from LSI.<\/p>\n<p>What LSI did was automatically create a \u201csemantic space\u201d for information retrieval.<\/p>\n<p>As the patent explains, LSI treated this unreliability of association data as a statistical problem.<\/p>\n<p>Without getting too into the weeds, these researchers essentially believed that there was a hidden underlying latent semantic structure they could tease out of word usage data.<\/p>\n<p>Doing so would reveal the latent meaning and enable the system to bring back more relevant results \u2013 and <strong>only<\/strong> the most relevant results \u2013 even if there\u2019s no exact keyword match.<\/p>\n<p>Here\u2019s what that LSI process actually looks like:<\/p>\n<div id=\"attachment_433397\" style=\"width: 540px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-433397 size-full b-lazy pcimg\" alt=\"LSI process flow chart\" width=\"530\" height=\"661\" data-sizes=\"auto, (max-width: 530px) 100vw, 530px\" data-srcset=\"https:\/\/cdn.searchenginejournal.com\/wp-content\/uploads\/2022\/01\/how-lsi-works-61e5886f0e5b2-sej.png 530w, https:\/\/cdn.searchenginejournal.com\/wp-content\/uploads\/2022\/01\/how-lsi-works-61e5886f0e5b2-sej-480x599.png 480w\" src=\"https:\/\/mailinvest.blog\/wp-content\/themes\/breek\/assets\/images\/transparent.gif\" data-lazy=\"true\" data-src=\"https:\/\/cdn.searchenginejournal.com\/wp-content\/uploads\/2022\/01\/how-lsi-works-61e5886f0e5b2-sej.png\"\/><span class=\"wp-caption-text\"><em>Image created by author, January 2022<\/em><\/span><noscript><img decoding=\"async\" src=\"https:\/\/mailinvest.blog\/wp-content\/themes\/breek\/assets\/images\/transparent.gif\" data-lazy=\"true\" data-src=\"https:\/\/cdn.searchenginejournal.com\/wp-content\/uploads\/2022\/01\/how-lsi-works-61e5886f0e5b2-sej.png\" alt=\"LSI process flow chart\"\/><\/noscript><\/div>\n<p>And here\u2019s the most important thing you should note about the above illustration of this methodology from the patent application: there are two separate processes happening.<\/p>\n<p>First, the collection or index undergoes Latent Semantic Analysis.<\/p>\n<p>Second, the query is analyzed and the already-processed index is then searched for similarities.<\/p>\n<p>And that\u2019s where the fundamental problem with LSI as a Google search ranking signal lies.<\/p>\n<p>Google\u2019s index is <strong>massive<\/strong> at <a href=\"https:\/\/www.google.com\/search\/howsearchworks\/\" target=\"_blank\" rel=\"noopener\">hundreds of billions<\/a> of pages, and it\u2019s growing constantly.<\/p>\n<p>Each time a user inputs a query, Google is sorting through its index in a fraction of a second to find the best answer.<\/p>\n<p>Using the above methodology in the algorithm would require that Google:<\/p>\n<ol>\n<li aria-level=\"1\"><strong>Recreate that semantic space<\/strong> using LSA across its entire index.<\/li>\n<li aria-level=\"1\"><strong>Analyze the semantic meaning<\/strong> of the query.<\/li>\n<li aria-level=\"1\"><strong>Find all similarities between the semantic meaning of the query <\/strong>and documents in the semantic space created from analyzing the entire index.<\/li>\n<li aria-level=\"1\"><strong>Sort and rank<\/strong> those results.<\/li>\n<\/ol>\n<p>That\u2019s a gross oversimplification, but the point is that this isn\u2019t a scalable process.<\/p>\n<p>This would be super useful for small collections of information. It was helpful for surfacing relevant reports inside a company\u2019s computerized archive of technical documentation, for example.<\/p>\n<p>The patent application illustrates how LSI works using a collection of nine documents. That\u2019s what it was designed to do. LSI is primitive in terms of computerized information retrieval.<\/p>\n<h2>Latent Semantic Indexing As A Ranking Factor: Our Verdict<\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-420779 size-full aligncenter b-lazy pcimg\" alt=\"Latent Semantic Indexing (LSI): Is It A Google Ranking Factor?\" width=\"291\" height=\"239\" data-srcset=\"\" src=\"https:\/\/mailinvest.blog\/wp-content\/themes\/breek\/assets\/images\/transparent.gif\" data-lazy=\"true\" data-src=\"https:\/\/cdn.searchenginejournal.com\/wp-content\/uploads\/2021\/09\/rf-definitely-not-30-614c56b8b46b2-sej.png\"\/><\/p>\n<p><noscript><img decoding=\"async\" src=\"https:\/\/mailinvest.blog\/wp-content\/themes\/breek\/assets\/images\/transparent.gif\" data-lazy=\"true\" data-src=\"https:\/\/cdn.searchenginejournal.com\/wp-content\/uploads\/2021\/09\/rf-definitely-not-30-614c56b8b46b2-sej.png\" alt=\"Latent Semantic Indexing (LSI): Is It A Google Ranking Factor?\"\/><\/noscript><\/p>\n<p>While the underlying principles of eliminating noise by determining semantic relevance have surely informed developments in search ranking since LSA\/LSI was patented, LSI itself has no useful application in SEO today.<\/p>\n<p>It hasn\u2019t been ruled out completely, but there is no evidence that Google has ever used LSI to rank results. And Google definitely isn\u2019t using LSI or LSI keywords today to rank search results.<\/p>\n<p>Those who recommend using LSI keywords are latching on to a concept they don\u2019t quite understand in an effort to explain why the ways in which words are related (or not) is important in SEO.<\/p>\n<p>Relevance and intent are foundational considerations in Google\u2019s search ranking algorithm.<\/p>\n<p>Those are two of the big questions they\u2019re trying to solve for in surfacing the best answer for any query.<\/p>\n<p>Synonymy and polysemy are still major challenges.<\/p>\n<p><a href=\"https:\/\/www.searchenginejournal.com\/semantic-search-seo\/264037\/\">Semantics<\/a> \u2013 that is, our understanding of the various meanings of words and how they\u2019re related \u2013 is essential in producing more relevant search results.<\/p>\n<p>But LSI has nothing to do with that.<\/p>\n<hr\/>\n<p><em>Featured Image: Paulo Bobita\/Search Engine Journal<\/em><\/p>\n<\/div>\n<iframe data-lazy=\"true\" data-src=\"https:\/\/www.fiverr.com\/gig_widgets?id=U2FsdGVkX18x7XQvttUTrv1oEqmGNGTgvvCUiUoJ\/AP4z\/UyMz8lXGOLpu15jIMxBbTR0gmD5uBoFvhC4KWeALQRp3h\/X\/AwcVD0K8Wj9H\/ZzYKzcCNHosB9oS4SCJJFWiN85P9ICAc4OgCoE\/wHKIY7CDkf2\/DQ1vqGvk4smVe5cRDEmrLPCWi4FC8p40VUhSmWQ5udCm0zoJtorgWv3vbDQw0kKYkwn39ozAnQXDe+YvWMxkLFWA+O3TFwkJvdkIK+\/AUSnRssPKt5WHY0FhNOxnSPcLslEL4G4\/RfP95ve99U+kRnDy3X+KtzdQLY+u935ghON\/o3UE4IMv9oN6JX9RnxzL\/LRcOgnHigxStSGPKsZYtnz8RWNVT\/rOLAibqiWJadC5MYHRbekF3eg6FOGrQGkXYbsn0+a5aovnlLCbLwIqY9fcS17UX8J235iQ6cdmHNbrPeS84CMm34RA==&affiliate_id=1052423&strip_google_tagmanager=true\" loading=\"lazy\" data-with-title=\"true\" class=\"fiverr_nga_frame\" frameborder=\"0\" height=\"350\" width=\"100%\" referrerpolicy=\"no-referrer-when-downgrade\" data-mode=\"random_gigs\" onload=\" var frame = this; var script = document.createElement('script'); script.addEventListener('load', function() { window.FW_SDK.register(frame); }); script.setAttribute('src', 'https:\/\/www.fiverr.com\/gig_widgets\/sdk'); document.body.appendChild(script); \" ><\/iframe>\n<br \/><a href=\"https:\/\/www.searchenginejournal.com\/ranking-factors\/latent-semantic-indexing\/\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Latent semantic indexing (LSI) is an indexing and information retrieval method used to identify patterns in the relationships between terms and concepts. With LSI, a&#8230;<\/p>\n","protected":false},"author":1,"featured_media":5714,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-5713","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-universe"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Is It A Google Ranking Factor? - mailinvest.blog<\/title>\n<meta name=\"description\" content=\"Technology is forever changing, and there are always new pieces of technology to replace obsolete ones. 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