Think back, for a moment, to the world before search engines.

It’s not surprising if you’re finding it hard to picture. Since Yahoo and Google arrived in the mid-90s, the ability to immediately trawl a vast database of information for answers to, well, pretty much anything has become a fact of life.

Semantic search is set to mark the next paradigm shift in our daily data engagement. Instead of matching phrases, like current search engines do, semantic search will provide answers to questions.

Not only will this make the experience of searching more natural, but it will also make the accuracy and utility of answers exponentially more impressive.

Read on for a quick guide to the next great technological innovation. Check out some of the revolutionary tech it’s going to power here.

A history of semantic search

While semantic search is set to become a defining feature of the technology of tomorrow, it has a long history.

Vector search, the system of relationship built between words and terms that helps to establish their meanings in particular contexts, has its theoretical roots in the 1950s.

Google patented their first semantic search product at the end of the 90s when Sergey Brin filed a patent for a system that extracts patterns and relations from a database.

Things really kicked into gear around 2013 when emerging technology like neural networks and machine learning meant vector search systems could be trained on large datasets. Google’s BERT algorithm brought this technology to the public in 2019

Why is semantic search different?

The search engine technology that arrived nearly 30 years ago and changed the world forever was lexical.

That means they take the lexical elements of a search query, the words and letters, and try to match them to items in the database being searched, namely the web pages on the internet.

This is a great tool because it allows you to sift through a huge database quickly, but it can still yield unhelpful or downright wrong results. This is because lexical search engines have no idea what you mean by your search term.

Semantic search, by contrast, tries to establish the semantic meaning of your search query and then searches the database for results that match that, rather than just the elements of the search query.

This helps to exclude the results that have the right words but are about something completely different, and to include results that answer your question but use a whole different set of words and phrases.

Why does any of this matter?

As our use of search engine technology has developed over time, our use of it has changed.

Initially we used it to return a set of results that matched an area of interest – “best cheap Paris hotels,” for example.

Now, we’re more likely to use search engines to answer a question–something like “What’s the best reasonably priced hotel in Paris?”

In the first instance, any page featuring the words “best cheap Paris hotels” would be returned and you’d need to do some more work to establish whether it was in fact cheap and did have any reasonable claim to being the best.

A semantic search engine would instead pull in lots more information based on an understanding of what your search query means. For example, the word “best” would signal it should consider customer ratings and reviews. The word “cheap” would signal it should compare prices when ordering search results, and the word “Paris” would instruct it to look for locations in the French capital.

So a semantic search engine could return a result for “Hotel Saint Andres” in the Saint Michel district alongside its five-star reviews and a limited time discount offer on rooms.

Importantly, semantic search can understand “best cheap Paris hotels” and “What’s the best reasonably priced hotel in Paris?” are essentially the same search query.

This is why platforms like ChatGPT 4o are able to answer questions so effectively – because they understand what you are actually asking.