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

This paper provides an extensive overview of the use of knowledge graphs in the context of Explainable Machine Learning. As of late, explainable AI ha…

Topics related to information management taxonomies posted by the author of the book, The Accidental Taxonomist.

Digital Science has completed the acquisition of metaphacts, which has become the newest member of the Digital Science family.

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

In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly...

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

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The story so far

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Using open data to make your own graph…

🔎🔬🔍 Improve your knowledge graph with GRAPE! 🦟🦠🪲 You may not be aware, but KGs can have topological oddities affecting your analysis and modelling…

Critical steps in improving the productivity of enterprise note-taking

Knowledge Graph Costs I just finished my primary research for a new paper on the costs and obstacles of adopting knowledge graph. The three themes that… | 10 comments on LinkedIn

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

RDFox v6.0 is here and it’s a big one! This release is landmarked by ground-breaking features including the new high availability setup and a total reimagining of the handling of named graphs. These features alone offer the opportunity for truly cutting-edge solutions, but when combined with the myriad of other improvements and the blistering capabilities of RDFox already in place, they allow you to step ahead of the curve. | 6 min read | Nov 29, 2022

Is Denoising Diffusion all you need?

Our most recent edition of the G-Research Distinguished Speaker Series took place at the Royal College of Physicians in Central London. Our featured speaker, Amy Hodler, is a graph evangelist, network science expert, and co-author of 'Graph Algorithms'. In her talk 'Average is a Lie - Using Graph Analytics to Improve Predictions', Amy discusses a range of topics, including: Graph queries Graph algorithms Relationships and structures in data Predicting behavioural change Machine learning Graph embedding Link prediction Data lineage Financial contagion Use of graph analytics in security Learn more about G-Research (https://www.gresearch.co.uk/) and view talks from speakers such as Professor Sir Martin Hairer and Wes McKinney in our Distinguished Speaker Series playlist. Interested in attending a future Distinguished Speaker Series event? Register your interest here now: https://events.beamery.com/gresearch/all-dss-events-mntauiaxr

#AI needs #KnowledgeGraphs to represent the physical world! See why AI needs @KnowledgeGraph for autonomous cars, for robotics, for smart homes and more...

And in the serendipitous discovery category today we have Google Cloud's "Enterprise Knowledge Graph", seemingly soft-launched a few days ago…

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.

The most common way to find answers on the Internet is to enter keywords in a web search engine (Google, Yandex, etc) and browse the returned results. However, in some cases the user is looking for a…

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How to invert the data integration problem within your organisation

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

This post by Michael Atkin is designed to demystify semantic standards and knowledge graphs for executive stakeholders

Graph neural networks (GNNs) have emerged as one of the leading solutions for ML applications. Most real-world data can be represented as graphs - see this blog for a comprehensive overview of what use cases are best solved with GNNs and their key advantages.