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
What’s New in RDFox v6.0? High Availability, Enhanced Named Graph Support, and so much more! | Oxford Semantic Technologies | 6 min read | Nov 29, 2022
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
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
PANEL: “KNOWLEDGE GRAPHS FOR THE PHYSICAL WORLD: WHAT IS MISSING?”
#AI needs #KnowledgeGraphs to represent the physical world! See why AI needs @KnowledgeGraph for autonomous cars, for robotics, for smart homes and more...
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.
Support search engines with knowledge and context — Part 1
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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Scalable Graph Learning in the Enterprise: Efficient GNN model training using Kubernetes and smart GPU provisioner
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.
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Semantic layers that are meant to serve as a single source of truth are fragmented, inflexible, and create a barrier to #data access
The analytics workflow is broken. Every time analysts have a new question, they must submit a request to the data engineering team to add a new column to a… | 249 comments on LinkedIn
Congratulations to @TigerGraphDB on being the first to successfully pass an LDBC SNB Business Intelligence workload audit on scale factor 1000. Their setup used the @AMD EPYC 9354 (Genoa) CPUs announced today. The results are available on the LDBC website.https://t.co/X3QocaTsRR pic.twitter.com/3F3J56wa96— Linked Data Benchmark Council (@LDBCouncil) November 10, 2022
500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.