What if your AI could not only retrieve information but also uncover the hidden relationships that make your data truly meaningful? Traditional vector-based retrieval methods, while effective for ...

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

GA release accelerates production streaming pipelines with real-time CRUD synchronization, reusable data flows, ...

An executive guide to artificial intelligence, from machine learning and general AI to neural networks. Read now This is a summary of the thesis taken by scientist, best-selling author, and ...

The Graph, the decentralized indexing system that works much like Google for blockchains, has introduced a data standard for Web3. Called GRC-20, the standard would define how information is ...

SurrealDB 3.0 launches with $23M in new funding and a pitch to replace multi-database RAG stacks with a single engine that handles vectors, graphs, and agent memory transactionally.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More What do you get when you combine two of the most up-and-coming paradigms ...

Knowledge graphs have existed for a long time and have proven valuable across social media sites, cultural heritage institutions, and other enterprises. A knowledge graph is a collection of ...

Graph database vendor Neo4j Inc. is teaming up with Snowflake Inc. to make a library of Neo4j’s graph analytics functions available in the Snowflake cloud. The deal announced today allows users to ...

Roughly 80% of enterprise data sits in emails, contracts, call transcripts, and PDFs where traditional databases can't touch it. Much of this "unstructured" data isn't ignored because it lacks value, ...