GraphNews

New release of neosemantics for #neo4j is now available (3.4.0.2). It includes model mapping and microinferencing capabilities. Watch this space for examples of use... https://t.co/il4UuzC5zF #RDF— Jesús Barrasa (@BarrasaDV) October 10, 2018

“#RDF4J v2.3 released, JDK9 compatible, Skolemization, IRI validation, improved order/limit performance, better TTL pretty-print, many bugfixes https://t.co/FJpPJrdYrp https://t.co/Y4k7kiIkWz #semweb #java”

Recently I decided to teach myself #React and #electron so I built out a IDE for running traversals and visualizing results for @apachetinkerpop enabled databases. If you want to try it out you can get it here: https://t.co/Yb6eaF6HKo #graphDB #graph— Dave Bechberger (@bechbd) August 23, 2018

If you find https://t.co/XZ7CIl9C8r interesting, knowledge graphs the next generation of search, & linked data a place where the web becomes a very useful database, you might want to check out this new site from the creators of https://t.co/XZ7CIl9C8r: https://t.co/bhnxXR1grI— Bill Slawski ⚓ (@bill_slawski) October 18, 2018

Old-school symbolic AI is sneaking back into modern machine learning AI, and the heritage of triples-based inferencing shows through in some of it. Nice short presentation by Imperial College London's Marta Garnelo: https://t.co/4nelxJmsYu pic.twitter.com/fSftB5Tqr9— Bob DuCharme (@bobdc) June 24, 2018

New blog entry "Extracting RDF data models from Wikidata". https://t.co/B5iWE4zWN7— Bob DuCharme (@bobdc) November 18, 2018

RDF was designed as a data interchange framework; what you do in the privacy of your own database is your own business— Dan Brickley (@danbri) September 16, 2018

Semantic Webby projects could often do with a dose of this. I'm afraid that careful UI creation suffers from semweb culture valuing generality over all else.Perhaps SHACL & ShEx shapes will provide better attachment points for UIs built over graph data? https://t.co/C1xvAoxPiU— Dan Brickley (@danbri) May 19, 2018

Gartner serves up 2018 Hype Cycle with a heavy side of AIImage Source: https://t.co/90NoF4IqvG#AI #MachineLearning #DeepLearning #BigData #Fintech #Insurtech #Marketing #Datascience #ML #DL #Robotics #HealthTech #IoT #tech Article Link: https://t.co/CtQlfve1Gm pic.twitter.com/Y5Xelzc6r4— AI (@DeepLearn007) August 21, 2018

New @ManningBooks early access book: "Graph-Powered Machine Learning" by Dr. Alessandro Negro @AlessandroNegro #MachineLearning #neo4j #AmazonNeptune #DataScience https://t.co/XpxZK3jw0g pic.twitter.com/xntecIh0Yh— John Dhabolt (@Dhabolt) October 17, 2018

Here are our slides for #stratadata on the progress we've made on scalable vulnerability discovery with code property graphs: multi-layered, extensible and with support for multiple programming languages: https://t.co/qbgK5PM28m pic.twitter.com/iRjLfr8VY2— Fabian Yamaguchi (@fabsx00) May 23, 2018

I think graphs are the correct mental model and API for deep learning -- but not graph of ops like a TF graphdef, instead, graph of layers. Recursive graphs of high-level building blocks.Which is also how deep NNs are visualized in pretty much every paper or textbook ever...— François Chollet (@fchollet) November 15, 2018

Sorry, @neo4j, but I gotta ask. What's wrong with #sparql? pic.twitter.com/JkrF7uck6V— Florent Georges (@fgeorges) May 15, 2018

I just published the latest version of Practical Gremlin in all supported formats. Another substantial update. Please see the change history for full details. https://t.co/UNJzfsUg3s … … https://t.co/mXGaOEe3q6 … … https://t.co/7YtyD2xQuR … … @apachetinkerpop @JanusGraph pic.twitter.com/EcokGbuwMN— Kelvin Lawrence (@gfxman) May 29, 2018

I’ve been saying this to paying customers for a couple years and now DataStax is just giving it away. Next thing you know everyone will have a nicely partitioned, massively scalable graph. And then what will have but loads of potential business value being realized. https://t.co/tJs4bUnfTD— GraphCurmudgeon (@GraphCurmudgeon) June 17, 2018

At long last! With this, we aim to bridge the gap between semantic web and graph database communities. Happy graph-querying! https://t.co/8al1uLi1aC— Harsh Thakkar (@Harsh9t) August 17, 2018

Presentation slides for my #iswc_conf blue sky ideas track paper "Make #Embeddings Semantic Again!", including my tongue-in-cheek depiction of the #semanticweb #layercake as of 2018: https://t.co/HC1gIA2Sqz pic.twitter.com/GpR68z5NFs— Heiko Paulheim (@heikopaulheim) October 15, 2018

Using @neo4j to analyse the madness that is Javascript package dependencies for my upcoming @ACCUConf talk. This is the dependency graph for what `create-react-app` spits out. Seems 4K isn’t enough to get it all in view at once :S pic.twitter.com/MPjmN7yKx4— I, Dom Davis (@idomdavis) April 3, 2018

The work from @SDA_Research member @Harsh9t and @dharmenpunjani on converting SPARQL queries to Gremlin path traversals has been integrated into the popular Apache Tinkerpop framework (master branch): https://t.co/XEctJ6Q6A5 - you can now run #sparql queries in @apachetinkerpop!— Jens Lehmann (@JLehmann82) August 17, 2018

Satya is excited about Microsoft's Cosmos DB momentum (>$100m in annualized revenue), given that it launched last year. 'I mean, I've been around databases for a long time and I've never seen a product that's gotten to this kind of scale this quickly,' he said— Jordan Novet (@jordannovet) April 26, 2018

I usually mention both as unique/key features of RDF: global identifiers and zero-cost merge— Martynas Jusevicius (@namedgraph) June 8, 2018

In this week's #twin4j @amyhodler interviews Alastair Green, @boggle, and @aethelraed about the GQL proposal that was published last week.https://t.co/HWlcwFIEwE#neo4j pic.twitter.com/xYwPXZcC55— Neo4j (@neo4j) May 27, 2018

New book: "Querying Graphs" by Angela Bonifati (@ang3ela), George Fletcher (@georgehiroshi), Hannes Voigt, and Nikolay Yakovets (@nikk186)https://t.co/VufBTdGdG3— Olaf Hartig (@olafhartig) October 2, 2018

Happening in March next year: the @w3c Workshop on Web Standardization for Graph Data.Mark your calendars and express your interest in participating!https://t.co/HbDEO3dxM4 https://t.co/PELY1dVkyT— Olaf Hartig (@olafhartig) October 15, 2018

Graph Neural Networks / Relational Networks are models worth studying. We wrote a pretty comprehensive review about them which I hope you will find helpful (code forthcoming!). https://t.co/D46XCkUIeb pic.twitter.com/Shw0FOhdIh— Oriol Vinyals (@OriolVinyalsML) June 12, 2018

JanusGraph 0.3.0 is released! Congrats to the 27 contributors who made the release possible. https://t.co/za3cH9pG6a @JanusGraph @linuxfoundation #TinkerPop #graphdb pic.twitter.com/5SxDMNXRsg— Jason Plurad (@pluradj) August 7, 2018

Why companies are turning to Graph? Jonathan Lacefiled from @DataStax gives us 4 main reasons: query performance, flexibility in data access, recency in data updates and visualization of patterns. #saegusmeetup pic.twitter.com/qsCtUEpvdk— Saegus conseil (@saegus_france) May 23, 2018

“Which #RDF graph partitioning technique gives better performance in cluster triplestore? checkout our empirical evaluation of RDF graph partitioning techniques in different architectures. https://t.co/wRlrR8lOSa #EKAW2018 #BigData”

Super excited! Our work on explaining knowledge graph relationships has been judged as the best paper at @iswc2018. Thanks to the reviewers who gave extremely valuable suggestions to improve our work. #iswc_conf https://t.co/lNMOdsIQFi#ibm @IBMResearch— Sumit Bhatia (@sbhatia_) October 12, 2018

Excited to learn that we won the VLDB 2018 Best Paper Award! "The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing" https://t.co/F5rR8ND5NQ @queryproc @semihsalihoglu @lintool @ozsu— Siddhartha Sahu (@sdht0) August 16, 2018