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With the introduction of entity SEO, paneles solares zaragoza several major components within Google’s search engine were altered.

It appears that many SEO professionals still operate under the rules of “search engine 2.0′, even though “search engine 3.0″ now follows a slightly different set of rules.

Entity SEO introduces vocabulary and concepts that originate from machine learning and information retrieval disciplines.

These terms may seem complex because they have not been simplified into their core meanings. Once we distill them, you’ll find the concepts are not overly complicated.

My goal is to construct a simple yet effective notional machine of how the latest search engines use entities.

More specifically, I want to illustrate how your understanding of SEO needs to be updated to reflect this new reality.

While understanding the “why” behind these changes might seem unimportant, many SEO professionals effectively “hack the matrix” by using their understanding of how Google interprets the web to their advantage.

In recent times, people have built million visitor sites and transformed Google’s understanding of subject matter by manipulating these concepts.

Refresher: How we arrived at search engine 2.0

Before exploring the differences between “search engine 2.0” and “search engine 3.0″’, let’s review the core changes from the initial version 1.0.

In the beginning, search engines operated on a simple “bag of words” model.

This model treated a document as a mere collection of words, neglecting the contextual meaning or arrangement of these words.

When a user made a query, the search engine would refer to an inverted index database – a data structure mapping words to their locations in a set of documents – and retrieve documents with the highest number of matches.

However, due to its lack of understanding of the context and semantics of both documents and user queries, this model often fell short of delivering relevant and precise search results.

For example, if a user searched for “jaguar” using a “bag of words” model, the search engine would simply pull up documents containing the word “jaguar” without considering the context.

This could yield results about the Jaguar car brand, the jaguar animal, or even the Jacksonville Jaguars football team, irrespective of the user’s intent.

With the advent of “search engine 2.0,” Google adopted more sophisticated strategies. Instead of just matching words, this iteration aimed to decipher the user’s intent behind their query.

For instance, if a user searched for “jaguar,” the engine could now consider the user’s search history and location to infer the likely context.

If the user had been searching for car models or resided in an area where Jaguar cars were popular, the engine might prioritize results about the car brand over the animal or the football team.