GraphNews

Taking data out of the restrictions of the relational database makes it easier to traverse your data to find connections—that is, if you map the data into the form of a graph with entities represented as vertices and relationships represented as edges.

Dame Wendy Hall: "Let’s not underestimate the vision or the task – this is a moonshot....a National Digital Twin, an ecosystem of digital twins, is going... 15 comments on LinkedIn

Amy Hodler is Director, Analytics and AI Program at Neo4j   Conventional anti-money laundering (AML) analytics fail to detect the hidden relationships that reveal criminal networks, says Neo4j’s Amy Hodler Catching money launderers is a huge and growing challenge. Criminal money laundering activities are often hidden in plain sight, within legitimate transactions. Money laundering now […]

DIG: Dive into Graphs A research-oriented library that includes unified and extensible implementations of algorithms for (1) graph generation, (2) self-supervised...

Rich Text, Lean Knowledge Bases: Knowledge Extraction from Financial Documents - my keynote presentation at the AAAI-KDF now available as recording, in...

We are excited to announce the launch of HealthECCO, a non-profit association that builds on the amazing work of the #CovidGraph Project. The core #Neo4j...

Nebula Graph 2.0 GA is here! Following hard work for nearly one year and several beta versions, Nebula Graph 2.0 is now generally available and can be ...

What’s the best way to get to know your customers? Learn how Wunderman Thompson understands their market using their identity graph.

At the first-ever Disney Platform Tech Showcase, Disney Advertising Sales highlighted the Company’s investments in technology and innovation – unveiling strategies and solutions around data-driven precision, premium ad experiences and frictionless transactions for marketers.

Getting a graph database to be performant and easy to use is very different from making a NoSQL (non-relational) database high-performing. Listen in as Todd Escalona of AWS talks with Marc Wangenheim, Senior Engineering Manager at Nike, about how the company powers a number of applications via a social graph, built on Amazon Neptune, which […]

A set of benchmark repositories for node classification on knowledge graphs (work in progress).

Did you miss the #frAIday talk with @FrankVanHarmele on boxology of design patterns for systems that learn and reason? Here is the link to the recording and slides! https://t.co/oXbqz9zqFq#AI #KnowledgeRepresentation #MachineLearning #symbolicAI #connectionistAI pic.twitter.com/CAqLnt2JeZ— ResponsibleAI@Umeå (@ResponsibleAIU1) March 15, 2021

Seeking to develop a multilingual Wikipedia where content can be shared among language editions.

Class 2 - Semi-Structured Information Repository Second in a series on the choices for capturing information and using knowledge in AI systems. Information-Centric Classification and Class 1 recap The previous blog in this series introduced the concept of an information-centric classification of AI

@linkedktk Have you seen the SHACL support in metaphactory? Not only visualisation for documentation, but also visual authoring: https://t.co/5mhJ1tcqYb

A look into the graph representations of r/Investing and r/WallStreetBets, often seen as the bears vs the bulls in the market. Hopefully…

I've written a blog which shows you how to embed a #3D #MindMap with hyperlinks in your homepage https://t.co/Y1ZUUreAio , an example is available at https://t.co/l8ap24ZcMc pic.twitter.com/5QWesXLaFt— Ingo Straub (@inforapid) March 14, 2021

Introducing a completely redesigned release with a unique user experience

There is a great deal of talk these days about using knowledge graphs to help manage large amounts of data. Learn the basics of knowledge graphs.

"The UI allows individuals with no previous knowledge of the Semantic Web to query the DBpedia knowledge base...." > Interface to Query and Visualise Definitions from a Knowledge Base @anelia12430996 & Hélène De Ribaupierre https://t.co/QGSJSEq4Ab pic.twitter.com/EIhZRVikK0— Aaron Bradley (@aaranged) March 15, 2021

📣 The code & datasets of our #CVPR2021 paper on #neurosymbolic explanatory interactive learning (NeSyXIL) is out!It involves a novel object-based neural concept learner & allows one to tell a DNN to not use "any gray box" to justify its classification.https://t.co/FcGg2LTs9z pic.twitter.com/IZt8622817— Kristian Kersting (@kerstingAIML) March 16, 2021

Knowledge Graph Question Answering using Graph-Pattern Isomorphism https://t.co/NMNc2N6kvx pic.twitter.com/VXsu2i7i9o— Aaron Bradley (@aaranged) March 16, 2021

"Information to Wisdom: Commonsense Knowledge Extraction and Compilation" from popular resources fortext extraction, e.g., #Wikipedia and scientific documents.(Razniewski et al, Tutorial at @WSDMSocial )paper: https://t.co/FiUedPOAzspage: https://t.co/YyuYX6xSCO pic.twitter.com/UvlM7bxqcW— WikiResearch (@WikiResearch) March 16, 2021

Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts https://t.co/tJzTg12Et8 pic.twitter.com/y7uEGwQbs7— Aaron Bradley (@aaranged) March 16, 2021

A Novel Paper Recommendation Method Empowered by Knowledge Graph: for Research Beginners https://t.co/K5TJEfEZcY pic.twitter.com/3JXpFUdds9— Aaron Bradley (@aaranged) March 17, 2021

KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph https://t.co/PQUHvgYT0C pic.twitter.com/Ss8UIyH1l6— Aaron Bradley (@aaranged) March 17, 2021

Need to link #KnowledgeGraphs? Our article on the #OpenSource link discovery framework LIMES (https://t.co/zSp8nSaDNG) is now available at https://t.co/Fy2JMNRKQE. With LIMES, we support #OpenScience on the integration of knowlege graphs @MAhmedSherif @kvndrsslr @mommi84— Axel Ngonga (@NgongaAxel) March 18, 2021

"Open Graph Benchmark: Datasets for Machine Learning on Graphs" including the 'ogbl-wikikg2' datastet obtained from @Wikidata.(@weihua916 et al, 2021)paper: https://t.co/5GK0rM1wY0page: https://t.co/eTLSlyDgRN #KDD21 competition: https://t.co/vfLZ73Sqja@harvard_data pic.twitter.com/SGO5LyPjKW— WikiResearch (@WikiResearch) March 18, 2021

NetVec: A Scalable Hypergraph Embedding System https://t.co/CHaT76MA7T pic.twitter.com/ZVChTtb0zH— Aaron Bradley (@aaranged) March 18, 2021