Author: Pat Lasserre
Neural search (also commonly referred to as vector search or semantic search) is hot — it seems like almost every day I see a new article written about it. For example, I recently saw these articles from Google, Home Depot, and Spotify.
That’s why I shouldn’t be too surprised by the number of people who have reached out to us to see if our Elasticsearch and OpenSearch k-NN plugins can accelerate their neural search applications.
In this post, I’ll briefly present a few of those applications, many of which need to search through large amounts of unstructured data — ranging from hundreds of millions to billions of items.
Traditional search engines use an inverted index along with something like TF-IDF or BM25 for ranking. They use sparse vectors, based on literal keywords, to find similar/relevant information.
As we wrote about in a previous blog post, these traditional approaches are limited in their understanding of user intent. For example, they could omit documents that don’t share many keywords with the query but are relevant documents.
Search is moving beyond this simple keyword-based search, to neural search, where user intent is better captured.
Neural search uses neural networks to better understand user intent and go beyond traditional exact-match keyword search.
Here are just a few of the neural search applications that people have talked to us about:
Traditionally, IP experts have spent a lot of time analyzing documents to come up with relevant keywords for a prior art patent search. Some of the keywords can be very subtle and can be missed if done manually.
Also, with datasets in this space growing rapidly, oftentimes in the billions of documents, relying solely on human analysis is not realistic or scalable.
That’s why IP experts are turning to neural search to efficiently sift through those billions of documents. Neural search provides the ability to quickly search through patent data by focusing on context, and not just keywords, to provide relevant references.
Figure 1 below is an example from a paper that shows how neural search can be used in prior art discovery.
In this application, candidates and job descriptions are mapped to a common embedding space to find potential fits. This can be used to recommend and rank candidates for a particular position, find similar candidates, or to find similar positions.
This application typically has large data requirements, on the order of hundreds of millions of documents.
Have you ever experienced the frustration of watching a video and later wanted to find a specific topic in the video, but you couldn’t remember exactly where it was in the video? If so, then video transcription with semantic search can save you from having to sift through the whole video to find it.
Basically, what this application does is break the transcribed video down into paragraphs that are semantically indexed. A neural search between the query and this semantically indexed content then helps you find what you’re looking for.
This is where companies like Google and Facebook analyze online behavior to categorize individuals. They then use this information to match groups of finely categorized people to individualized advertisements — creating a targeted advertisement. This is done by creating embeddings for target customers and advertisements and mapping them to a common embedding space such that a similarity search can be performed to find the right match.
If you’re using neural search in your application, I’d love for you to share a bit about it in the comments section.