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3.2 Google and Latent Semantic Indexing (LSI)
In the last unit of the course, we showed you how a search engine attempts to find relevant documents for a search query by locating pages in its index that match the search query - that is, pages that contain the specific words we entered. However, the process is rather more complex than this, largely because of an innovation on the part of the world’s leading search provider, Google.
In order to return more relevant results for the user, Google has begun to employ a method called ‘Latent Semantic Indexing’ when indexing documents on the web. Although this method is not used universally by all search engines, it is likely that other search engines will begin to factor this (or a similar) method into their algorithms in the future.
Note that Google does not rely entirely on LSI for finding relevant results. However, according to noted SEO experts like Aaron Wall, Google has been using LSI ‘for a while’ and has ‘recently increased its weighting' . This means that while traditional keyword based search queries are still relevant - i.e., Google still tries to retrieve documents that contain the specific search terms or keywords you use - Google’s search algorithm has begun to place more importance on LSI when attempting to determine and retrieve relevant documents for a specific search query.
So what is LSI and how does it differ from a standard keyword search? In essence, LSI is a method for retrieving documents that are relevant to a search but that may not contain the specific keyword entered by the user.
For example, in a traditional keyword based search, if I enter the search phrase ‘used cars’ into the search engine, it will only return documents that mention those actual terms somewhere on the page. It will not return web pages that mention terms that we normally consider to be closely related to our search query, e.g. ‘second hand’, ‘vehicles’, ‘automobiles’, and so forth (unless these pages also happen to use the keyphrase ‘used cars’).
When using LSI, on the other hand, the search engine finds a means to locate pages that contain related terms as well as our specific keyphrase. Therefore, our search might also return pages that only mention ‘second-hand automobiles’ as well as pages that specifically mention ‘used cars’.
As you can see, then, LSI allows the search engine to return documents that are outside our specific search phrase, but that are still relevant to our search. It begins to approximate how we actually use language in real life, where we are aware of alternative terms and synonyms for words, and for this reason should prove to be more useful to the searcher than a standard keyword search.
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