Graph databases are finding new use cases in sales, ecommerce, healthcare, financial services, fraud detection and much more.

SeeNews talks to Ontotext’s CEO Atanas Kiryakov about the latest developments in semantic technology and graph technology as well as GraphDB

Proper modeling of a graph database can be challenging. Let’s check how ChatGPT will perform with a semi-real case.

To use this feature, you just need to add 'with("Neptune#ml.inductiveInference")' to your Gremlin query with Amazon Neptune. Happy graphing, folks!...

With the mission of building the most user-friendly graph-as-a-service that unlocks smarter insights for all, our product and engineering teams at TigerGraph have been working hard to elevate TigerGraph Cloud to the next level of ease-of-use and enterprise readiness.

Success stories, shared at Ontotext’s KGF22, about building knowledge graphs in Industry, Healthcare, Life Sciences, Financials

This blog post was on the front page of Hacker News for a day — Link It’s been almost nine months since I left Dgraph Labs, a company that I spent

Let Andreas Blumauer, CEO of the Semantic Web Company, introduce you to a Graph-based Semantic Layer in Enterprises. Read more!

Excited to share our paper on graph representation learning in biomedicine and healthcare, published today in Nature Biomedical Engineering. In this…

Amazon Neptune is a fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. With Neptune, you can use open and popular graph query languages to execute powerful queries that are easy to write and perform well on connected data. You can use Neptune for […]

Limited Preview Query Translation Tool Is Now Available for Developers to Learn and Participate in the Development Process...

A powerful idea has been slowly building for many years now, originally known as the Semantic Web, and then later as Linked Data. This idea has finally... 27 comments on LinkedIn

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NNeo4j, JanusGraph, and TigerGraph are the top 3 Native Property Graph Database Management Systems, according to DB-Engines ranking, but…

It was a thing long before big tech firms used it to work out who knew who, explains AWS

Signal AI unveiled its new tool, a data structure that constantly tracks the major and minor events for companies that course through the news sphere each day.

Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics. — Timothy G. Mattson, Senior Principal Engineer, Intel Corp Expertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. Massive Graph Analytics will be beneficial to students, researchers, and practitioners in academia, national

Emerging graph database benchmarks are already helping to overcome performance, scalability and reliability issues.

TigerGraph unveiled a new tool that provides users with a dedicated, open source environment for building machine learning models with graph databases.

TigerGraph's ML Workbench should enable data scientists to build deep-learning AI models using connected data directly from the business.

The Studio Search platform takes a portion of the federated graph and makes it searchable.

Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologie...

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This week, enterprise data catalog provider data.world announced both a $50 million Series C round and a new suite of data catalog management capabilities

With the right data fabric, people and algorithms can make better decisions and reduce the risk of data misuse or misinterpretation.