GraphNews

During Microsoft Ignite 2021, Microsoft unveiled new Azure AI and Cognitive Services aimed at solving enterprise-scale challenges.

Graph Networks are extremely useful tools to help understand the graph data that's all around us. In this episode, I'm going to explain what some of the latest advancements in graph networks are and how you can leverage them to build your own graph network in a few lines of Python code. This space has matured so much that there is never a single library to discuss, there are always multiple competing options (which is a great thing). We'll be weighing the pros and cons of the Deep Graph, Graph Nets, and PyTorch Geometric library as well. I'm particularly interested in Graph Networks because...

I have written a lot on how to create good taxonomies. But what's the case for having taxonomies in the first place? I excerpted 21 introductory slides...

The UX Methods project explores ways to bring the benefits semantic knowledge graph technologies to small and medium scale content collections.

The last few weeks have been busy in the Enterprise Knowledge Graph (EKG) space. This blog will review the key events and put them in…

We have a new paper showing that #GraphNeuralNetworks are state-of-the-art for representations for #ElectronicHealthRecords data!! EHR data is inherently... 46 comments on LinkedIn

"Birds of a feather flock together"

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There is a lot of mystique surrounding knowledge graph that it can sometimes be daunting to approach graph technology. I am here to let you in on a little secret, that you don't need to worry about all the jargon and the tech behind them to understand fundamentally what graph is and why people are talking about it. Check out the video to learn more (and take part in the giveaway)! Stay in touch: LinkedIn: https://www.linkedin.com/in/ashleighnfaith/ Direct Message: isadatathing-at-gmail.com Resources: Relational compared to graph: https://neo4j.com/developer/graph-db-vs-rdbms/ Graph database...

A couple of beautiful things happened during this week! 1. My pytorch-GAT project amassed 712 stars over the last week! Which is a beautiful signal to... 33 comments on LinkedIn

Although artificial intelligence capabilities are improving daily, it is not always easy to put the AI rubber on the road – especially when it comes to understanding AI’s contextual data and problem-solving approaches. How about bringing in some “real” intelligence? Graphs are a typically human way

A new interconnected ecosystem for research is shaping up, and machine learning is just the tip of the iceberg.

I just added a new tool Kalicube Reverse Knowledge Graph Lookup. Look up any kgid. Now THIS has started me down a deep, deep rabbit hole (or was it the...

The largest funding round to date in the graph market is good news not just for TigerGraph, but for the market at large

Ever wish it was just easier to learn about taxonomy? Or how about teaching the Girl Scouts about data science? Or maybe finally getting your friends to understand what you actually do at work? Join me in this step by step walk through on how to make a taxonomy and how you can use your junk-drawer to help your teams, family, and friends learn with you. No jargon, no strict rules, just having fun while we play with data. Kit Materials: (I got mine from the Dollar Tree but any odds and ends will do) 1 pack of colored pencils 1 pack colored markers 2 packs "pompoms" 1 pack beads of the same sh...

Ever wish it was just easier to learn about ontology and knowledge models? Or better yet, how to use a taxonomy to BUILD an ontology? Or how about teaching the Girl Scouts about data science? Or maybe finally getting your friends to understand what you actually do at work? Join me in this step by step walk through on how to take a taxonomy and make a pizza ontology to help your teams, family, and friends learn with you. No jargon, no strict rules, just having fun while we play with data. Part 1: Making the taxonomy https://youtu.be/bLNWyfc2jvQ OWL file for the original Stanford Pizza Ontolo...

Knowledge graphs: The secret of Google Search and now XDRhttps://t.co/kZOvSz3Kke#Infosec #Security #Ceptbiro #Cybersecurity #KnowledgeGraphs #GoogleSearch #XDR— Rene Robichaud (@ReneRobichaud) February 17, 2021

Having been involved in the Semantic space for more than a decade and a half, I've seen quite a few arguments that seem to be eternal. Do you use upper ontologies or not? Is SHACL better than OWL? Property Graphs vs.

So much to explore in this thesis by @nickvosk, much of which is focused on "making structured knowledge more accessible to the user by describing and contextualizing [knowledge graph] facts" > Supporting search engines with knowledge and context https://t.co/sIQjQY3zri pic.twitter.com/C89v4YyrO5— Aaron Bradley (@aaranged) February 17, 2021

Existing note-taking apps like OneNote or Evernote are unsatisfying to me, especially when it comes to ordering and structuring your notes and keeping control over your data. This is why I have...

MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks Wang et al.: https://lnkd.in/gs2g2mk #MachineLearning #ChemicalPhysics...

I'm so excited to share this! 🚀✨ My latest research project was to model the interactions between drugs and various biological targets in the body to... 15 comments on LinkedIn

Graph Convolutional Networks (GCNs) combine deep learning with feature diffusion to produce useful node embeddings. The embeddings are constructed by taking...

Tracking the historical events that lead to the interweaving of data and knowledge.

Graph database advantages are abundant, which is why enterprise adoption has trended upward the past few years. Check out what the top advantages are to organizations.

A Google patent describes how Google may read pages on the Web to build a large data graph that can be used to answer queries.

The recording of my talk on Theoretical Foundations of Graph Neural Networks is now live (+ slides are in the description)! 🕸️ Join me as I derive GNNs...

Great!It will be even more interesting if done for @wikidata , where granularity is much high (item-level, not article-level)Not only more interesting but often more accurate. See why: https://t.co/6ZVZG9yTwF #SPARQL https://t.co/yGSLwMhUQC— Ivo Velitchkov (@kvistgaard) February 21, 2021

The Growing Importance of #MetadataManagement: https://t.co/p44BXJW6y5 ——————#BigData #DataScience #AI #MachineLearning #MDM #DataGovernance #DataManagement #DataStrategy #DataLiteracy #Metadata #DataCatalogs #KnowledgeGraphs #LinkedData #Provenance #DataLineage #DataDiscovery pic.twitter.com/fPjoC89H47— Kirk Borne (@KirkDBorne) February 9, 2021