GraphNews

We’ve all seen the signs in front of McDonald’s announcing “Over X Billion Served” and have watched the number rise over the years. But tracking how many burgers are sold every day, month, or year is a relic of the past. Today ask: Do we know where each consumer buys her burgers? At what time? What does she drink with it? What does she do before or after buying a burger? How can we satisfy more of her needs so that she keeps coming back? Datagraphs capture this information, helping to reshape competition in every sector. Leaders must invest in upgrading their data architecture to enable a real-time, comprehensive view of how consumers interact with their products and services so that they can develop unique ways to solve customer problems.

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

iResearch

Learn about the Four Principles of Semantic Parsing: The Parser Principle, The Data Uncertainty Principle, The Data Entropy Principle, and The Principle of Deferred Semantics.

How Airbnb is leveraging graph neural networks to up-level our machine learning

Data Modeling and Analysis using Semantic Web Technologies

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In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of...

Scientific Reports - Graph-based representation for identifying individual travel activities with spatiotemporal trajectories and POI data

Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to...

Ultipa: Reimagining Data Processing with Graph Augmented Intelligence Solutions/Service Company-Beijing, China; Singapore; California, U.S.A

Fine-tuning innovations from many vendors

Why provenance is the key to AI success

What are the three core features that give knowledge graphs their capabilities and how do they generate value for four key players?

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

Thoughts on semantic layers, data transformations, data apps and the future of the data stack.

AWS developed the Deep Graph Knowledge Embedding Library (DGL-KE), a knowledge graph embedding library built on the Deep Graph Library (DGL). DGL is a scalable, high performance Python library for deep learning in graphs. This library is used by the advanced machine learning systems developed with Trumid to build a credit trading platform.

In a distributed world where data is federated in a Mesh internally and you value comes from how you collaborate externally the most…

Happy to announce that we just published our #linkeddata extension for #VSCode. Visualize, transform, validate and query your #rdf directly on your files...

Posted Grigor Aslanyan, Software Engineer, Google Patent documents typically use legal and highly technical language, with context-depend...

At IKEA we are building a knowledge graph to improve the overall experience of our customers in the physical and digital space. I like to…

You thought you knew all normal forms? Well, think again: There is also “graph normal form,” based on mathematics going back 25 years.

Co-authored with Meghana Negi, Jose Mathew, Jairaj Sathyanarayana

Ultipa has recently released v4.0 of its flagship graph database product, on top of its v3.0’s already worldleading performance. Ultipa 4.0 introduced p

Recent advancements and hot trends, July 2022 edition

Network architecture plays a key role in the deep learning-based computer vision system. The widely-used convolutional neural network and transformer treat the image as a grid or sequence...