New from Google Research! REALM: https://t.co/kS2oTyxAAjWe pretrain an LM that sparsely attends over all of Wikipedia as extra context. We backprop through a latent retrieval step on 13M docs. Yields new SOTA results for open domain QA, breaking 40 on NaturalQuestions-Open! pic.twitter.com/DYDFX69Td8— Kelvin Guu (@kelvin_guu) February 11, 2020

Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it.  All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks. But the nature of graph data poses challenges when it comes to *buzzword alert* scalability. So why is this, and are graph databases capable of scaling? Let’s see... In the following, we will define what we mean by scaling, take a closer look at two challenges potentially hindering scaling with graph databases, and discuss solutions currently available. What Is the “Scalability of Graph Databases”? Let’s quickly define what we mean here by scaling, as it is not “just” putting more data on one machine or throwing it on various ones. What you want when working with large or growing datasets is also an acceptabl

Dr Nicolas FigayDigital Enterprises Organisation and Collaboration around Manufacturing and Product Data2w · EditedEmerging Landscape of #graphs related technologies: required move from da facto standards to #ISO open standard?

Do you want to learn more about #knowledgegraphs and data-centric architecture? @AlanMorrison put together a list of resources #datacentric #dataarchitecture #semantics #LinkedData #GraphDatabase

So yeah. Today Gartner named Graphs on their top 10 trends for data in 2019, stating that "graph DBMSs will grow at 100 percent annually through 2022." 💪💪💪 pic.twitter.com/LsCg38eyON— Emil Eifrem (@emileifrem) February 18, 2019

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How can investigation into money distribution and tracking funds cross the borders of blockchains?

based search systems do not reflect the semantics of individual input words of search queries. For example, a query for the word “house” would not return records for the words “building” or “real estate”. How can such relationships be represented in a technical system? One approach is to include synonyms. Search engines like Elasticsearch provide methods to integrate synonym lists. However, a list of synonyms itself is required for configuration.

We just released PSPS: an open source tool allowing Linked Data sites based on GitHub repositories. Like GitHub pages but self-hosted and with SPARQL and RDF. Check it out: https://t.co/tBu6xvA2Qa - As you might have guessed: that's the software powering https://t.co/olmahLZFvb— FactsMission AG (@FactsMission) January 10, 2019

Stardog 6.2 just shipped with scalable virtual graph caching, better Kubernetes integration, support for Amazon Redshift, and many new optimizations . Read on for the details.

Feeling SHACL'd to your desk at home these days? Then try out SHACL Play!, a "free online SHACL validator for RDF data" from Thomas Francart bit.ly/2QfpbQ5

Financial fraud is growing and it is a costly problem, estimated at 6% of the Global Domestic Product, more than $5 trillion in 2019.

Financial fraud is growing and it is a costly problem, estimated at 6% of the Global Domestic Product, more than $5 trillion in 2019.

triple store which we will query with SPARQL. If you are not yet familiar with knowledge graphs and reasoning, you can read an introduction published on

Short comparision of #SPARQL and #GraphQL by @RubenVerborgh. One SPARQL Query can go against multiple SPARQL endpoints in a natural way - that is not equal to schema stitching in GraphQL because there is no need for schema assumptions of different #API's: https://t.co/RWYuvBSIqX— Francis Opoku (@fraopo) December 23, 2018

Grab your free copy of the brand new O'Reilly book, "Graph Algorithms: Practical Examples in Apache Spark & Neo4j" – a practical guide to graph analytics.

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Joshua Shinavier from Uber coming up with a broad definition of knowledge graphs #us2ts2019 pic.twitter.com/0LbMMJQP6S— Gaurav Vaidya (@mrvaidya) March 11, 2019

A comprehensive collection of recent papers on graph deep learning - DeepGraphLearning/LiteratureDL4Graph

VLog, a new rule based reasoner on #KnowledgeGraphs, with #opensource implementation on #Github #iswc_conf #research #sfotwareengineering h/t

Cypher for Gremlin adds Cypher support to any Gremlin graph database. - opencypher/cypher-for-gremlin

Albany, US, 2019-Jan-23 — /EPR Network/ —Market Research Hub (MRH) has actively included a new research study titled “Global Graph Database Market” Size

Jude Gomila, who previously sold his mobile advertising company Heyzap to RNTS Media, is taking on a new challenge — building a “knowledge base” that can fill in Wikipedia’s blind spots, particularly when it comes to emerging technologies and startups. While Gomila is officially launching Golden today, it’s already full of content about things like […]

Learn more about the Closeness Centrality graph database algorithm, which measures how a central a node is within its cluster.

Graph databases and analytics are getting ever more accessible and relevant