Google uses machine learning and graphs to deliver search results. Most search engines do not. Weaviate wants to change that. X. Trending. Apple's iOS 26 and iPadOS 26 public betas are ...

Machine learning is great for answering questions, and knowledge graphs are a step towards enabling machines to more deeply understand data such as video, audio and text that don’t fit neatly ...

First is Node2Vec, a popular graph embedding algorithm that uses neural networks to learn continuous feature representations for nodes, which can then be used for downstream machine learning tasks.

The idea is that graph networks are bigger than any one machine-learning approach. Graphs bring an ability to generalize about structure that the individual neural nets don't have.

The cloud’s place in the data environment is growing, and TigerGraph wants to bolster its role. Today, the company rolled out several new features so cloud users can deliver more analytics and ...

Empowering Machine Learning with Knowledge Graphs Rapid data collection is creating a tsunami of information inside organizations, leaving data managers searching for the right tools to uncover ...

Graph databases are proving a powerful tool in enabling the analytical techniques that AI and machine learning rely on.” George Anadiotis, Linked Data Orchestration/ZDNet: ...

Diffbot is a startup focused on using artificial intelligence to better provide companies information found on the internet. The core product is a knowledge graph they claim has mapped “over 10 ...

Bringing knowledge graph and machine learning technology together can improve the accuracy of the outcomes and augment the potential of machine learning approaches. With knowledge graphs, AI language ...