NEWS https://t.co/LK4SRbrCZD releases a new version with ❺ @BioSchemas types for representing life sciences dataA big step towards improving #findability of life sciences web resources@SSirarat @egonwillighagen @gray_alasdair #FAIRdata #bioinformatics➡️https://t.co/ZTq3T5FfyK pic.twitter.com/7fpfQmVd5w— ELIXIR Europe (@ELIXIREurope) July 9, 2021

Visualizing a codebase! How to maintain a better overview? Simply take a bird's eye view! The GitHub Repository Visualizer to fingerprint a codebase ...

Finally!! After a couple of years of writing, and building on a decade of research and practice, our book "Designing and Building Enterprise Knowledge ...

Not all information needs to be seen at first sight. Child drawings allow you to expand a parent node and see more elements. See the #graph at the level...

Visualising a code base, as a graph ! Nice experiment by Github’s Office of the CTO https://lnkd.in/dPZaycCp...

Something that I think is beginning to emerge is the close connection between networks (graphs), fractals, and quantum mechanics. We tend to see the world... 15 comments on LinkedIn

Recommended good read: Filtration Curves for Graph Representation (KDD 2021) Paper: https://lnkd.in/dGfJ_TR Code: https://lnkd.in/d-YCXZT #ai #machinelearning...

Merkle Science is a Singapore-headquartered blockchain data analysis firm that helps crypto companies, financial institutions, and government agencies detect, investigate, and prevent illegal activities that involve cryptocurrencies. Over the past couple of years, Merkle Science has been trying to visualise their data and make it easier to understand through use of a graph database. Unlike […]

How the Semantic Web vision reincarnated in thousands of Linked Open Data datasets and millions of Schema.org tagged webpages

[Article] Interactive Graph Stream Analytics in ArkoudaFully #openaccess @ https://t.co/9h0J69f8Q3#streaminggraphdata#graphstreamsketch#trianglecounting#Arkoudaframework#algorithms pic.twitter.com/AfxyflYZ7k— Algorithms MDPI (@Algorithms_MDPI) July 27, 2021

A major update to the graph-notebook project (https://t.co/a7gh3wpOy2) was just released . Connect to any TinkerPop enabled graph database and see your query results as text and diagrams as well as use Python code. SPARQL and openCypher supported too! @apachetinkerpop #graphdb— Kelvin Lawrence (@gfxman) July 29, 2021

Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform.

DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection https://t.co/Op5RrTPR9y pic.twitter.com/yWMlDniKmJ— Aaron Bradley (@aaranged) July 26, 2021

How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction https://t.co/zwBLmn1FtH pic.twitter.com/U9bJEw4qLv— Aaron Bradley (@aaranged) July 27, 2021

"Improving Inductive Link Prediction Using Hyper-Relational Facts" from hyper-relational knowledge graph such as #Wikidata.(Ali et al, 2021)paper: https://t.co/FfdhZJHxqzcode: https://t.co/uURI4qi2Jj.. pic.twitter.com/6HFbh59Na6— WikiResearch (@WikiResearch) July 27, 2021

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