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Semantic SEO brought on faster and better results. Or did it?

January 20, 2018

Smart SEO reflects all the modern algorithms involved in delivering the most suited search results. As intelligent software increases its capabilities, these backstage software becomes more and more nuanced, more human-like. Machine learning empowers artificial intelligence and increases the quality of its interactions with humans. Software becomes more likely to understand and process human requests. This, applied to Internet search, becomes semantic SEO.

What is semantic SEO?

Semantic designates the meaning of words. When you type just one word in search, it pretty much can have any meaning listed in the dictionary. Regardless of the searcher’s intent, Google will return the most relevant results based on the standard word significance.

Once the users type two or more words, things start to change. The words’ meaning becomes contextual. In a human conversation, your interlocutor would instantly know what you are talking about when instead of “step” you say “wooden step”. Google also refines the two sets of results accordingly. This is due to the use of artificial intelligence in order to deduct the meaning of the query. Matching search results with the searcher’s expectation is based on semantic search.

Not only is this system trying to raise above dictionary-like search, but it also tries to guess the reasoning behind your search entry. Are you looking for interior design wooden step pieces? Or is it that you are looking for a brand named like this? Are you looking for carpentry tips?

When did semantic SEO appear?

It may be a paradox, yet we searched Google for the earliest “semantic SEO” results. They seem to belong to 2013, when Moz featured a post on this newly coined notion, that ended up in numerous subsequent articles on other websites.

Considering how in 2013 Bing and Google had already started implementing this entity-based queries search, now we are looking back at three years of development. See here how an SEJ article from 2014 announced that “search is changing” and explained how semantic SEO works.

Semantic SEO equals smart SEO. Search Entity Optimization replaced Search Engine Optimization in the view of those who closely followed this change.

While other elements remain the same, regardless of keyword-based search or entity-based search, the search engines that implemented this smart algorithm consider they provide better, faster search results than before.

Are search engines better in 2016?

We used the plural in our heading, yet all you and we can think about is Google. The king of all search engines, whether dictatorial or not, Google rules most of the searches initiated into the digital realm.

However, its entire activity follows patterns and rules. Or, in other words, depends on algorithms. The company selects and sets them up, based on certain considerations. The users get the results the way these algorithms tune them up.

Google set rules against fake content, against replicated content and all kinds of practices that led to deceiving results at the end of a search query. They also penalized the lack of mobile optimization. And so on. Acting like a guardian of the digital space, they constantly regulate best practices, while making possible for the users to reach websites and pages.

Due to the fact that in all machine learning frequency and quantity matters, search engines changed. Meaning that they assume things on behalf of their human users, based upon numbers. It does not matter whether your particular request is unique or different, they will assume your intent was “A” because thousands of other people meant “A” when typing the same combination of words. Therefore, you will receive the “A”-tailored list of search results.

Semantic, better than keyword-based search?

As we mentioned above, even when interpreting your combination of search words in a semantic way, an AI-based search engine will still provide a mechanical result. That’s what machines do. For example, with localization in place, you will receive the most geographically close results of restaurants, even if in fact you wanted to see a top of the best restaurants in the country.

The weird thing is that while some features refine themselves, others get completely lost. The more specialized search engines get, the more annoying they become once users need to go another way than they are used to.

Semantic or search entity optimized functions can give exceptional results for commonly searched terms. The more predictable the meaning is, the better. The problem is with the exceptions. However, machine learning still has a long way to go. There is perhaps enough time to tune ML until capable of exceptional integration, too.

Why does specialization inhibits some abilities?

Perhaps this also a human trait. I remember our friend’s kids being able to answer questions on basic physics or biology just by using their common sense when they were preschoolers. They couldn’t answer the same questions later, when studying these subjects in school because they “haven’t learned these things yet”.

In a similar way, I couldn’t find a movie with the help of Google in 2016. I had forgotten the title, therefore I tried various combinations of key elements from the movie’s content. The results frustrated me so much, that I dropped the search altogether. Why am I using this example? Because I had found the same movie somewhere in 2006 – and I remember it all perfectly. Shame I couldn’t keep the title in mind, as well.

Since now Google tries to guess what is on my mind when I enter the search terms, checking for those words does not count on “common sense” anymore. The search engine’s AI employs what it has learned. And apparently when trying to find a lost movie title, this does not work.