A knowledge graph captures the semantics of a particular domain using a set of definitions of concepts, their properties, relations between them, and logical constraints that are expected to hold. Knowledge graphs consolidate and integrate an organization’s information assets and make them more readily available to all members of the organization. There are many applications and use cases that are enabled by knowledge graphs. Information from disparate data sources can be linked and made accessible for to answer questions you may not even have thought of yet. Information and entities can be...

Company Achieves “Industry First” Results in New Graph Database Performance Benchmark

New website of oorlogsbronnen.nl about WWII in the Netherlands is launched today. Congratulations to Lizzy Jongma and her team with this impressive achievement...

Discover how a Product Knowledge Graph can help you create an immersive e-commerce experience and sell more products.

The Austrian provider of knowledge graph and text mining technology Semantic Web Company has announced the opening of its new U.S. office in Boston.

Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. However, there has been little work on interpreting them, and specifically...

Are you evaluating enterprise knowledge graphs (EKGs) for your business? This was the deck of a talk I gave for the AI+Knowledge Graph conference held ...

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What’s advancing the world of graph databases, and what’s slowing us down?

My engineering friends often ask me: deep learning on graphs sounds great, but are there any real applications? While Graph Neural Networks are used in recommendation systems at Pinterest, Alibaba and Twitter, a more subtle success story is the Transformer architecture, which has taken the NLP world by storm. Through

The metaphactory platform enables knowledge workers to create and gain meaningful insight into their data with one comprehensive software solution.

Learn how DeepWalk treats creating graph embeddings as equivalent to creating word embeddings

Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering https://t.co/AdDke9AtZ0 pic.twitter.com/QXPucOK1Xo— Aaron Bradley (@aaranged) September 3, 2020

Great job to Prasun Gera for presenting our joint research on Traversing Large #Graphs on #GPUs with Unified Memory, with Hyojong Kim, @piyusch, & Hyesoon Kim, in virtual Tokyo @VLDB2020 #DataScience @NJIT @NJITYingWu https://t.co/J7V4K94VSF pic.twitter.com/LjsrKrqHJY— David Bader (@Prof_DavidBader) September 7, 2020

We got this year's @AmazonScience AWS ML Award for our work with @befcorreia on #protein design using #geometricdeeplearning Will help to take #masif to the next level https://t.co/Ec7t2g7nqV pic.twitter.com/OrHGwHp1cE— Michael Bronstein (@mmbronstein) September 9, 2020

"PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs." with an evaluation over three datasets on the #Wikidata Knowledge Graph.(@debayan Banerjee et al, 2020)https://t.co/cTCq4EhrGP pic.twitter.com/Ci1yn2CPDp— WikiResearch (@WikiResearch) September 8, 2020

Some good progress happening on the RDF* mailing list, towards a de-facto standard for representing statements about statements in a user-friendly syntax. See whole thread for context if interested https://t.co/S6N9w3XLH7— Holger Knublauch (@HolgerKnublauch) September 7, 2020

(1/5) Thank you everyone who came to Graph-n-Code livestreams with @SonicDMG and I. 🙏This thread has all the links you need for FREE access to:📌 The code📌 The Images📌 The bookWe are cooking up more livestreams; stay tuned! pic.twitter.com/QFbvdSiAO9— Denise Gosnell, PhD (@DeniseKGosnell) September 8, 2020

"We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection" > FANG: Leveraging Social Context for Fake News Detection Using Graph Representation @ngnvnhng et al. https://t.co/mTJDQW9bYo pic.twitter.com/vVeYFToMN9— Aaron Bradley (@aaranged) September 8, 2020

SPEX introspects knowledge graphs in SPARQL endpoints, using RDF's self-describing nature to give a better understanding of its schema. Once the schema is available, SPEX can be used to browse instances of this data and follow links to other data. https://t.co/B0WTz6zsQT— Tim Finin (@timFinin) August 24, 2020

Very cool: a new Knowledge Graph Search tool by @maxxeight & @MerkleAllows you to:• View entities associated with a query• Extract Knowledge Graph IDs• See scoring for different results+ preview the SERP (favourite feature)Test + bookmark here: https://t.co/WcCENJNG11 pic.twitter.com/KokprslthX— Brodie Clark (@brodieseo) September 10, 2020

Can we use #graphneuralnetworks when the graph is not given? In a new blog post I show that a new type of "latent graph learning" architectures can be thought of as a modern take on #manifoldlearninghttps://t.co/p40Sod9EOr pic.twitter.com/jw7RsKuiMi— Michael Bronstein (@mmbronstein) September 10, 2020

Just added >11M #OpenCitations to #COCI, for an overall amount of >733M citations currently available in our dataset – it can be queried via #REST API & #SPARQL endpoint and can be fully downloaded as a dump (available on #Figshare)+info at https://t.co/nxSlZGkb3G #OpenScience pic.twitter.com/FZhHYN782y— OpenCitations (@opencitations) September 7, 2020

Water networks are graphs. And, if poorly designed, vulnerable. Storing their topology in a #graphdatabase helps identifying components & weaknesses. This @graphileon demo was built on top of #neo4j, without writing any code. #lowcoding #YourAppIsAGraphhttps://t.co/2WW3YCRQwF pic.twitter.com/bJ9pYjRTkt— Graphileon (@graphileon) September 12, 2020

In this week's #twin4j, @adamcowley hows us how to build a Knowledge Graph from our Slack archiveshttps://t.co/isibvvCAZ0#neo4j pic.twitter.com/BCm6o0fcNd— Neo4j (@neo4j) September 12, 2020

Rule-Guided Graph Neural Networks for Recommender Systems https://t.co/Rt2TRzVllt pic.twitter.com/52A8tvKNz5— Aaron Bradley (@aaranged) September 10, 2020