GraphNews

Exploring decentralized finance on the blockchain with a graph database

Welcome to the launch of Visual Query Builder (VQB) on GraphXR! This is a preview of VQB on an existing Neo4j database supplied with Twitter data from the US presidential election, where we build basic Cypher queries using drag and drop, no-code visual building blocks. Schedule a free live-training below to learn more today! 👩‍💻💡 https://meetings.hubspot.com/kineviz/vqb-building-cypher Special thanks to Alice Benedict & the Kineviz team for creating this preview of VQB!

Learn how to combine Selenium and SpaCy to create a Neo4j knowledge graph of the Harry Potter universe

Library for deep learning on graphs

And how much can a simple schema do for you?

Relational Graph Convolutional Networks: A Closer Look https://t.co/JOg3a2YtUV pic.twitter.com/XZmYnRAVxY— Aaron Bradley (@aaranged) July 22, 2021

Learn how to visualize family trees and work with live data using Memgraph and Cypher

Some of the handiest tools in an ML engineer’s toolbelt are vector embeddings, a way of representing data in a dense vector space. An early example of the

Knowledge Graph embedding provides a versatile technique for representing knowledge. These techniques can be used in a variety of applications such as completion of knowledge graph to predict...

Hey RDF folks, what is the state of the art in making visualizations of RDFS/OWL vocabularies, shapes, of the kind that would make people familiar with UML feel comfortable?— Dan Brickley (@danbri) July 20, 2021

"WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset", collected by pairing Wikipedia articles with a subgraph from the Freebase knowledge graph.(Luyu Wang et al, 2021)paper: https://t.co/FKmpfBARe8data: https://t.co/hS1xbWj9aI pic.twitter.com/HOQxRoc5we— WikiResearch (@WikiResearch) July 21, 2021

ThingFO v1.2's Terms, Properties, Relationships and Axioms - Foundational Ontology for Things / Luis Olsina https://t.co/c91oOm2SAg pic.twitter.com/3YxDhtG1iN— Aaron Bradley (@aaranged) July 21, 2021

Information Integration using the Typed Graph Model / Fritz Laux & Malcolm Crowe https://t.co/b3cAQt9c5U pic.twitter.com/fGm1V309Ul— Aaron Bradley (@aaranged) July 21, 2021

This post is part of our “Data Engineers of Netflix” series, where our very own data engineers talk about their journeys to Data…

Cf. Damion Dooley, @griffiemma, @plbuttigieg, FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration https://t.co/ARpniFL1FQ https://t.co/MXIfFbAeNc pic.twitter.com/qkHeMpcxQe— Aaron Bradley (@aaranged) July 21, 2021

In my previous post in which I discussed names, I also led in with the fact that I am a writer. Significantly, I did not really talk much about that particular assertion, because is in fact comes with its own rabbit hole quite apart from that associated with names and naming.

Zero-shot Visual Question Answering using Knowledge Graph https://t.co/R9VNiLFdZE pic.twitter.com/8TbY3lkgGu— Aaron Bradley (@aaranged) July 13, 2021

Similar Cases Recommendation using Legal Knowledge Graphs https://t.co/I9lTyMwsMF (yet another cc: for you @EmekaOkoye :) pic.twitter.com/Sqrm5uGhFF— Aaron Bradley (@aaranged) July 13, 2021

Using graphs, we can uncover interesting relationships for the SME to find similar patents and maybe uncover clusters or groups of patents…

Learn about a Google patent that enables searchers to explore connections between related entities in different types of media.

"We demonstrate that this approach significantly reduces the cognitive load required to users for visualizing and interpreting a knowledge graph...." > Pattern-based Visualization of Knowledge Graphs @lguspree et al. https://t.co/S1DuVfNlxc pic.twitter.com/EEH9o8A8eA— Aaron Bradley (@aaranged) July 7, 2021

SeaNet -- Towards A Knowledge Graph Based Autonomic Management of Software Defined Networks https://t.co/XZSHPF44Hj pic.twitter.com/p9MOGGxhB1— Aaron Bradley (@aaranged) July 6, 2021

Visual Question Answering (VQA) is concerned with answering free-form questions about an image. Since it requires a deep semantic and linguistic understanding of the question and the ability to...

How MUM helped improve access to high-quality COVID-19 vaccine information worldwide.

We wanted to see if the sensor data can be brought together through the Node Red low-code/no-code framework to feed the data into RDFox, an in-memory triplestore and then data summaries periodically pushed to a central, larger triplestore.

Due to the fact that the chapters are related to each other, I realized my book is better represented with a graph than in a linear formatYou can see the "Values" chapter is the core, so many other parts come from that pic.twitter.com/lFsxG0U7M6— Sarah Drasner (@sarah_edo) July 13, 2021

Graph neural networks are intimately related to partial differential equations governing information diffusion on graphs.

Wouldn't it be great if AI could reason with commonsense knowledge?Check out our latest blog post on a new question answering model, QA-GNN, that jointly reasons with language models and knowledge graphs, by @michiyasunaga!https://t.co/6b6e7ZWCba— Stanford AI Lab (@StanfordAILab) July 13, 2021