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The transition from “search engine 2.0” to “search engine 3.0” has brought significant changes, particularly with the introduction of entities.

This article explores these shifts, the impact of entities on modern SEO and how to adapt your strategies to thrive in this new era.

Building your own SEO ‘notional machine’

In my early years of learning to code, a teacher introduced an impactful concept known as “notional machine,” which reshaped my approach to programming and, later, SEO.

Simply put, it’s a developer’s approximate mental model of what happens inside the computer when they click run.

My teacher emphasized that the more detailed and accurate this mental representation was, the better equipped I would be to tackle new problems.

The most successful programmers were those who had developed the most accurate and reliable notional machines!

Drawing a parallel to SEO, when we absorb new concepts, examine a case study, or 印度 色情 observe the impact of a change, we are continuously updating our mental model (our own notional machine) of how search engines work.

The difference between skilled SEO’s and unskilled SEOs is that they can drive results because they can pull solutions from a more accurate model.

Research in the field of expertise conducted by Anderson Ericsson provides substantial evidence to affirm this point.

His studies on expertise reveal that those who excel in their fields possess superior and more readily accessible mental models.

These models enable them to understand the intricate cause-and-effect relationships, distinguish what truly matters in a complex scenario, and perceive the underlying processes that are not immediately apparent.

With the introduction of entity SEO, 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.

Introducing personalized search results – considering factors like user history and location – significantly enhanced the relevance and precision of search results. This marked a significant evolution from the basic “bag of words” model to “search engine 2.0”.

Search engine 2.0 vs. 3.0

As we transitioned from “search engine 1.0” to “search engine 2.0”, we had to update our mental models and change our practices.

The quality of backlinks became crucial, prompting SEO professionals to abandon automated backlinking tools and seek backlinks from higher-quality websites, among a number of key changes.

In the era of “search engine 3.0”, it’s clear that the mental shift to accommodate these changes is still in progress.

Many concepts from the 2.0 era persist, largely because practitioners need time to observe the correlation between their adjustments and the subsequent outcomes.