Why semantic search engines are the search engines of the future
Introduction
With the recent advances in AI there has been an explosion of a new type of search engine, the semantic search engine. Unlike traditional search engines, semantic engines actually have an understanding of the words in the query and user intent (semantics) and generate search results using that understanding. Imagine you wanted to find the conversation you had with your friend about the best desserts you’ve had in your life. Today you might go into your phone and search “best dessert” and hope that one of you said those words. With semantic search, you can just type best desserts and the engine would be able to find your conversation you had with your friend about the amazing tiramisu you had in Italy because it understands that tiramisu is a dessert. Think about this technology applied to school notes, meeting transcripts, doctor notes, legal documents. The ability to just ask questions like we would to another person and immediately find the most relevant results is a massive step forward in this data age.
A brief history
Search engines have been around for decades and have enabled people to find the information they need with relative ease. They are arguably one of the most powerful and important pieces of technology in existence and allow us to sift through billions of webpages, as in the case of Google, or millions of products, such as on Amazon. Often people don’t consider how prevalent search engines truly are, but ask yourself the question: when was the last time you went to a webpage or used an app that didn’t have a search bar. A product today that served content would be nigh unusable with search.
But many of these search engines were built on keyword search, meaning when you type: “red shorts” into a site like Neiman Marcus for example, the search engine tries to match the words red and shorts to the product titles and descriptions in its database. It has absolutely no understanding of what the words “red” or “shorts” are or mean, to the search engine it was equivalent to asking for “slippery popsicle”. It’s just using the search words to directly match to its catalog.
Enter semantic search engines
In a simple example, our “red shorts” query to a traditional engine would probably not return us any “maroon” colored shorts, even though the colors are very similar because there is no similarity between the words red and maroon to a traditional search engine. However, our semantic engine understands that maroon and red are very “close” words because they stand for colors that are similar, and would thus return us some maroon colored shorts when we asked for red. To put it another way, semantic search engines not only consider the direct words themselves, they consider the intent behind the words, similar to how we as humans process words as we hear them.
Why is this a big deal?
I cannot stress to you enough about how important semantic search engines are. They are without a doubt the next evolution of search thanks to their ability to combine all the past factors like keywords with an understanding of intent. Semantic search engines understand what the user is getting at and can more accurately pull back relevant results. They unlock the ability to start searching vast amounts of data, but also unlock the ability to understand vast amounts of data. As we progress through this technological boom we start to see systems mimic human intelligence more closely.
While data has long since been an asset class in and of itself, the value of the asset is further increasing over time. Collecting data and converting it to text is no longer something that cutting edge companies are doing, it is becoming a standard, a necessity for companies to do, lest they fall behind their more data rich competitors.
What are the implications for the business world
There are already a number of industries that are and should be looking to employ semantic search engines in their operations. Any industry that has a ton of text based data to sift through are immediate first candidates, for example the healthcare, financial, and legal sectors. But today we are starting to see these new types of search engines enter into almost every industry from oil and gas, to education, and even e-commerce.
This really illustrates a larger shift in how data is being processed and understood. As we unlock new natural language processing capabilities the data that a business captures becomes more and more important because the tech can now extract more meaning from it than before. As a business owner it can revolutionize your internal operations by giving you the ability to search through every sales pitch your team does, search and understand every HR report, or search through all social media posts about your company far better than the built-in search engines those platforms support. Semantic search engines are the keystone to unlocking an entire new wave of business intelligence.
Conclusion
Semantic search engines are the search engines of the future. By understanding the context and meaning of search queries, they are able to return more accurate and relevant results. This enables users to find the information they need quickly and easily, resulting in improved user satisfaction.
If you are in an industry that deals with a lot of data and could benefit from better organization and searching capabilities of that data, I would highly recommend you start considering semantic search engines as a future part of your industry. They are an immediate and tangible boost to operations and can drastically improve the efficiency of many organizations.
My agency PressW has been building these semantic search engines for clients all across the US and in different industries. From our experience working in various fields like insurance, finance, healthcare, we’ve learned that each of these engines needs to be properly tuned and fitted to each client’s data. We know that he true value of the system shines when the data is properly prepared and integrated into the system. You have to take the time to really understand a client’s data, how it can be restructured to best serve these engines, and then the proper data cleaning and processing pipelines implemented to guarantee long term success.
If you or anyone you know are interested in building a powerful custom semantic search engine to power their business, don’t hesitate to reach out to me directly at tarun@pressw.co
Thanks for reading!
Note: Every image (minus the PressW logo) in this article were generated using Stable Diffusion on playground.ai
