A Look into the History of Semantic Search
Google does not want to disappoint its users and Google does not appreciate when someone else is disappointing its users. The search engine giant provides the best experience to its users. This is the reason why it is making use of machine learning and artificial intelligence to understand the behavior of its users. This is reflected in all algorithm updates and SEO guidelines it has been changing.
Back in 2010, search engine optimization was all about getting as many backlinks as possible and including keywords in the content and URL. SEO guys used to understand how the search engine was taking queries and generating results. And then they used to do reverse engineering to figure out how they could get their content ranked higher.
As always, Google is evolving and so is search engine optimization. You cannot rely on identifying keywords only. It is important to understand the meaning of your keywords. You must provide rich information and keyword usage in your content should be natural with the understanding of user’s intent.
This age belongs to semantic search and semantic SEO. So, in this article, you will learn what semantic search is. You will also learn about the history of semantic search.
Instead of just reading a string of words, Google has started understanding several things to provide the most accurate results to its users. This is semantic search. The things search engine is trying to understand include user’s search intent, user’s query context and the relationships between words. Or, we can say that it is trying to understand natural language like we humans do.
Let’s understand this with an example. Someone asked you a question – What is the fastest animal on the earth? The next question you are asked is – How fast is it? You know that Peregrine Falcon is the fastest animal on the planet. You also understand that ‘it’ in the second question refers to the fastest animal on the planet – Peregrine Falcon. However, before 2013, search engine was unable to understand what that ‘it’ referred to i.e. search engine was unable to understand the context of the next question.
However, with semantic search, the search engine can distinguish one entity from other entities such as people, places and things. The search engine considers multiple factors to understand the intent of the user. Some of these factors are:
- Search history
- Spelling variations
- Global search history
Let’s understand this with an example. You have searched for “big cats” in 15 days. And now when you Google ‘Jaguar’, the search engine will assume that you are searching for one of the five big cats, not Jaguar cars.
So, you should create a semantic SEO strategy not just SEO strategy.
A look into the history of Semantic Search
The Knowledge Graph
The Knowledge Graph was introduced in 2012. This was the first step towards recognizing the importance of considering context and entities over strings. Here is what Google said in 2012: “things, not strings.” Large-scale changes are made in the algorithms with the introduction of The Knowledge Graph.
The Knowledge Graph is a huge database of public information that collected information from public domain such as the star cast of “Inception”, distance to the sun etc and properties of entities such as people have parents, siblings, birthdays, occupations etc.
Back in 2013, the Hummingbird update marked the beginning of the era of the semantic search. According to that update, content produced with the user’s intent and context in mind will be ranked better.
RankBrain was launched in 2015. This machine learning system is using smart AI for query analysis and it is a ranking factor as well. Just like Hummingbird, RankBrain also understands the intent of the user. However, RankBrain makes use of machine learning. This algorithm learns and analyzes search results. Even a page not containing the query exactly is deemed to be a “good response” to user’s query.