What is neural search and how does it work?

What’s the first thing that comes to mind when you think of search?

You probably envision someone on a website, entering a query in a search box, and their search results appearing based on a search engine matching the keywords with the right content in its network architecture. After all, how else would a search engine identify information than by instantly matching up keywords?

Keyword-based search has been the status quo forever. But now, new technologies are beginning to change what goes on behind the scenes in search applications. The days of keywords being the sole way to track down information on the Web are fading. Modern new ways of searching are being used with keyword search to create more-accurate search results, — plus better search experiences for users.

Here’s a rundown of the latest evolving search methods currently in use by companies around the globe:

Traditional keyword search

With old-school keyword search — the kind you’ve come to know and love — you’ll definitely get plenty of search results. Granted, rules must be created for the search engine ahead of time so that it can effectively make sense of the data and point you to the best information it can put its digital fingers on in all available repositories. Depending on how your keywords are matched, your search results may not always be super accurate or optimally ranked for your purposes, though they’ll be laid out for you in the blink of an eye so you can expediently click on what appears to be a good bet.


AI-powered search

Traditional keyword search methods and capabilities have been steadily evolving along with major advances in the world of artificial intelligence (AI). Most search-engine use cases still rely on the keyword-matching method; others are more actively making way for newcomers. To improve the quality of search results, AI-powered search has joined baseline keyword search. With AI, search result relevance is improved by gathering and considering data on users’ activities. AI search utilizes a range of modern technologies, including machine learning and natural language processing (NLP), to better understand what information each user needs to locate. 

Vector search

Many newer AI-based search engines incorporate vector search, which uses machine-learning models that detect semantic relationships.

Search vectors are mathematical representations of text (this applies to both search queries and indexed information, such as product information and web pages). Vectors let search engines understand the context of entered text. In the process of converting words into vectors, the meanings are encoded.

For instance, a keyword-based search engine doesn’t necessarily know that “warm,” “heat wave,” and “summer” could be related. By contrast, vector-based search engines do understand these types of word relationships, which allows them to outperform keywords-alone search.

Because of this added dimension, vectors are the preferred choice for tasks like automating synonyms, clustering documents‍, detecting meaning and intent in queries, and ranking search results.

Compared with the output of keyword search engines, vector search can be slower, it may scale poorly, and it’s expensive. 

Neural search

Then there’s neural search, the hottest emerging search method, poised to take the search world by storm. It sounds like something ”brainy,” and indeed, it encompasses fancy, interconnected-node-based “thinking” on the part of algorithmic components known as neural networks (also referred to as artificial neural networks; ANNs). Neural search combines the power of vectors to accurately represent data with quick performance and self-learning abilities.

The advent of neural search is one outcome of the rollout of Big Data, without which there would be insufficient information floating around in order for a search engine to succeed in analyzing accurately. The available modern data sets that our society has been amassing allow neural networks to sift through ample tons of info (much of it unstructured data) and assimilate it for a wide range of use cases.

Understanding semantics

Neural search facilitates improved semantic understanding of what people want when they’re searching. It leverages AI to detect relationships among people, content, and data, as well as connections between user interests and current and past search queries.

It can also learn arbitrarily complex representations from data and utilize relationships between data points. This gives it an advantage when it comes to understanding the semantics of a search query.

Due to being able to utilize both semantic and keyword retrieval methods, superior understanding of searcher intent translates to provision of more-relevant results. 

Flexible, independent, always learning

Neural search is also adaptable: it can be used with any type of data, including images, videos, audio, and 3D information. A vector representing an image, for instance, can contain information about its shape, color, and content, which is all helpful data for matching it with other images.

Neural search facilitates the delivery of relevant search results along with a reduced need for  ongoing management. For instance, it eliminates the necessity of the majority of synonyms and language rules. That means website search managers don’t need to babysit and fine-tune their search functionality by adding synonyms, formulating complex search rules, coming up with keyword-stuffing titles, or plugging in additional language packs.

And instead of relying on the making and updating of rules for a machine learning model, you can start with a trained neural network, and the model can then get progressively better “educated.” And because neural search is based on learning algorithms, it can adjust to changing real-world conditions over time (by fine-tuning). 

In short, neural search is:

  • Superior at understanding search queries
  • Powerful
  • Flexible
  • Ideal for utilizing in situations where system training data and input continually change, such as in ecommerce 

Neural networks and machine learning

Neural networks are also considered a form of machine learning — the branch of artificial intelligence (AI) that uses data and algorithms to replicate the way humans learn. 

With machine learning, data gets more refined over time. For instance, if someone searches for a topic and then starts another search for something seemingly related, a machine-learning-oriented system could assume that the two searches are related and suggest that the searcher check out material that encompasses both topics. With the integration of additional queries processed, the search engine learns and gets better at serving up accurate search results the next time.

Subset: deep learning

Deep learning is a subset of machine learning (and apparently, in case you were wondering, there is also such a thing as “shallow learning”).

The concept of deep learning was born and grew up based on humans’ understanding of how neural networks function. In 1943, scientists created a computer model based on the way neural networks operate in a human brain. Their goal was to duplicate the human thought process  using algorithms and mathematics.

Today, deep learning’s computational algorithms provide a robust framework for a variety of programming approaches. 

How is deep learning related to neural networks? And what about its impact on search quality?

A neural search engine relies on deep-learning algorithms to provide accurate search results as well as an excellent search experience for the user. 

A search engine consists of an indexer, a query processor, a retrieval algorithm, and a ranking algorithm. The big difference with a neural search engine is the retrieval part which uses machine learning to determine a measure of similarity between the query and search results. In this way, neural search engines can actually understand concepts, which can lead to better performance particularly for long tail queries where keyword/result pairs don’t perfectly match.

Deep neural networks

Deep learning, neural networks…it was inevitable that we’d get to the next variation of word soup variation: deep neural networks.

Deep neural networks are pretty much what they sound like: artificial neural networks imbued with more depth: additional layers tucked in between the input and output layers.

The focus of deep learning is how machines can learn by using the deep neural network computing model.

The concept of deep neural networks with several layers has recently become a highly successful and popular research topic in machine learning due to excellent performance in benchmark problems and applications.

Neural search is simply deep-neural-network-powered information retrieval. Neural search leverages the power of deep neural networks (DNN) to build every component of a search system.

What’s the latest thinking?

That sums up recent goings-on in the various types of online search technology.

So where is relevant search headed next? Check back in about a decade, when optimization made possible by the merger of full-text keyword and vector search technologies will have flourished, ensuring more-accurate search results than either a traditional search platform or vector search can currently provide alone.

Meanwhile, there’s still something you can do if you want to improve your site search sooner than sometime in the next decade, like right now. 

Check out NeuralSearch 

What’s that something? Check out NeuralSearch, our newly developed search technology, which uses vector search in concert with neural hashes to deliver fast, accurate search results for your users. It’s allowed us to combine the speed of traditional keyword search with the accuracy of neural search in a single API

With this game-changing acquisition, we can now provide true hybrid search for your website or app, combining full-text AI keyword search and vector-based semantic search for each user query. Yes, you can get state-of-the-art search results for your users or customers in milliseconds, regardless of the scale or query throughput.

Find out how you can take advantage of our cutting-edge search algorithm to improve your company’s search and bottom-line metrics, starting now. Contact our team and successfully end your search for the right search solution!

About the authorCatherine Dee

Catherine Dee

Search and Discovery writer

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