Onton Releases Ontology 1: A Neurosymbolic Search Model That is 2.7x More Accurate than the World’s Best E-commerce Search Engines
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Onton Releases Ontology 1: A Neurosymbolic Search Model That is 2.7x More Accurate than the World’s Best E-commerce Search Engines

August 2, 202649 views3 min read

This article explains how Ontology 1, a new search engine from Onton, uses a combination of AI techniques to understand complex product searches better than existing systems. It's a beginner-friendly look at neurosymbolic models and how they improve e-commerce search.

What is Ontology 1 and why is it a big deal?

Imagine you're shopping online and you want to find a specific product. You might type something like, "I need a blue, wireless, noise-canceling headphones under $100." This kind of search is tricky for regular search engines because they don't always understand what you're really looking for. That's where Ontology 1 comes in. It's a new kind of search engine created by a company called Onton that's much better at understanding what people want when they search.

What is a Neurosymbolic Model?

Let's break this down into simple parts. Neuro means "brain-like," and symbolic means using symbols or words to represent ideas. So, a neurosymbolic model is like a smart brain that combines the strengths of two different types of artificial intelligence (AI) systems.

One type of AI is like a very good guesser — it can learn patterns from lots of examples, but it might not always understand what it's really doing. The other type is like a very good logician — it can think through problems step-by-step, but it might not be good at learning from examples. Ontology 1 combines both of these strengths to create something that's better at understanding complex searches.

How Does Ontology 1 Work?

Think of Ontology 1 like a very smart assistant who can understand not just what you say, but also what you mean. When you type a search query, Ontology 1 does a few things:

  • It understands context — Like when you say "blue headphones," it knows you're looking for a specific color, not just any headphones.
  • It handles multiple types of information — It can understand text, images, and even prices all at the same time.
  • It learns from examples — The more searches it sees, the better it gets at guessing what you want.

Imagine if you had a friend who was great at reading your mind. They could understand that when you say "wireless headphones," you mean something that doesn't have wires, and when you say "under $100," you mean something affordable. Ontology 1 does something similar, but with computers.

Why Does This Matter?

For consumers, this means better shopping experiences. When you search for products, you're more likely to find exactly what you're looking for, quickly. For businesses, it means they can improve how customers find their products, which can lead to more sales and happier customers.

For example, if someone searches for "blue wireless headphones under $100," a regular search engine might show them all kinds of headphones, including some that aren't blue or are over $100. Ontology 1 is much more accurate at showing only the products that match what the person really wants.

It's like having a personal shopper who always knows exactly what you're looking for, even if you don't say it perfectly.

Key Takeaways

  • Ontology 1 is a new search engine that uses a combination of two AI techniques to better understand complex searches.
  • Neurosymbolic models combine the pattern recognition of neural networks with the logical reasoning of symbolic AI.
  • It's more accurate than existing e-commerce search engines, according to independent testing.
  • It works with text, images, and other data types to understand what users want.
  • It improves shopping experiences by helping people find exactly what they're looking for faster.

As AI continues to improve, we're seeing more systems like Ontology 1 that can understand us better, not just by looking at keywords, but by truly understanding what we mean. This is a big step forward in making technology more helpful and user-friendly.

Source: MarkTechPost

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