"This is what we need to do. It's not popular right now, but this is why the stuff that is popular isn't working." That's a gross oversimplification of what scientist, best-selling author, and ...

In the realm of Artificial Intelligence (AI), knowledge graphs stand as a crucial innovation, particularly influential in areas like machine learning and natural language processing (NLP). These ...

These past few months have not been kind to any of us. The ripples caused by the COVID-19 crisis are felt far and wide, and the world's economies have taken a staggering blow. As with most things in ...

While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge ...

A knowledge graph, is a graph that depicts the relationship between real-world entities, such as objects, events, situations, and concepts. This information is typically stored in a graph database and ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

Even though it probably affects our lives every single day, most of us have no idea what a “knowledge graph” is. Asking your favorite voice assistant what the weather will be like tomorrow? That’s ...

In 2006, Google patented a Browseable Fact Repository, which was an early version of what would develop into Google’s Knowledge Graph. It was a collection of facts related to entities, with ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract ...