Author(s): Patrick Meyer

Originally published on Towards AI.

As promised by many analysts, the automatic creation of knowledge graphs should have allowed us to reach the Holy Grail of knowledge access. Information extraction techniques have evolved significantly over the last few years, thanks to advancements in artificial intelligence and, in particular, machine learning technologies, to the point where generalization is possible. Despite these advances, there are still many obstacles to overcome before the expected ease of use is achieved.

Top highlight

Photo by Shannon Potter on Unsplash

In the beginning, was the Subject, then the Predicate, and the Object (SPO). These three elements are the simplest form, allowing us to represent all the knowledge of the world. In the continuity of this RDF (Resource Description Framework) triplet, knowledge graphs have regained popularity following the publication of the article by Amit Singhal, then senior VP in Google’s research teams. Published on May 16, 2012, it introduces a major change in their search engine: “Introducing the Knowledge Graph: things, not strings”.

So today I’m really excited to launch the Knowledge Graph, which will help… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI