GraphNews

A beautiful compilation of the exciting research that happened in the world of Graph ML as well as the predictions of where the field is heading to according...

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I would rate this app as the most useful app since MS Excel :) Regardless of if you are familiar with Neo4j or not......

From GCNs to R-GCNs: encoding the structure of Knowledge Graphs with neural architectures (examples in NumPy code)

From protein folding to new antibiotics, Graph ML methods shine in biochemistry and drug design applications.

Similarity Computation via Graph Neural Networks

How to solve the practical problems when building a real Enterprise Knowledge Graph service

My main talks on Graph Neural Networks in 2020 1. Introduction to GNNs https://lnkd.in/gF_NZ-5 Slides: https://rb.gy/yabvud 2. Recent developments in...

I'm in love with GNNs. Yet another amazing application in computational biology. How about detecting poly-pharmacy (using multiple drugs at the same time...

A beautiful example of how GNNs (graph neural networks) can be leveraged in massive-scale (billions of nodes!) production systems - such as the recommender...

Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective Lamb et al.: https://lnkd.in/dE3ZkMF #TrustworthyAI #KnowledgeRepresentation...

Semantic Linked Knowledge Web Data Graphs?

This is my third annual post on Enterprise Knowledge Graph (EKG) trends. You can also find my 2019 and 2020 posts on this blog, and I…

The final video for this year - Graph Convolutional Networks (GCNs)! The most cited paper in the GNN literature. As promised I'm continuing to work on...

AutoGL an Auto-ML Framework & Toolkit for Machine Learning on Graph AutoGL (Auto Graph Learning) is able to automatically handle all stages of graph learning...

Google has published a new way of pre-training a language model which is augmented using a knowledge retrieval mechanism, that looks up…

From protein folding to new antibiotics, Graph ML methods shine in biochemistry and drug design applications.

New Pizza Tutorial for Protege 5. Includes basic ontology development plus new sections for SWRL, SPARQL, and SHACL.

Graph neural networks, and geometric deep learning in general, are getting ever more important and we've seen trends on big conferences that back up that...

The purpose of a knowledge graph is to answer a user's questions. Some of the questions may be known upfront, while some questions users may never think of themselves.

Today’s post is about Google algorithm, commonly defined as the PageRank algorithm.

Despite being what can be a confusing topic, graph neural networks can be distilled into just a handful of simple concepts. Read on to find out more.

Modelling complex relationships in RDF graphs

Top 5 SEO predictions for 2021: it's time to re-think your content marketing strategy while focusing on continuous innovation and AI.

Hi all, I have developed new algorithms for managing semantics (i.e. logical assertions) in knowledge graphs - faster algorithms for automated deduction...

Trend 1: Smarter, faster, more responsible #AI. Gartner analyst Rita Sallam shares the top 10 #data and #analytics trends for 2020. Read more. #GartnerSYM #Trends

We are proud to announce DBpedia Archivo - an augmented ontology archive and interface to implement FAIRer ontologies.

AWS open-sources Graph Notebook to make working with graph databases easier - SiliconANGLE