GraphNews

We are proud to announce the release of #JanusGraph 0.4.0 with CQL OLAP support, performance improvements for pre-fetching of properties, and many updated dependencies: TinkerPop, Cassandra, HBase, Bigtable, and BerkeleyDB! Download now: https://t.co/tEkjTojUs2— JanusGraph (@JanusGraph) July 11, 2019

We just launched official #JanusGraph @Docker images to simplify production deployments and testing! Check out the images at https://t.co/Vo44Bokwpu and see the docs for more info: https://t.co/VLaLwR6jcI— JanusGraph (@JanusGraph) May 8, 2019

This is what the resultant 100 retail outlet isochrone map looks like, built using Spatia and @rapidsai #cuGraph SSSP using @OrdnanceSurvey Open Roads as road graph + drive times estimated from @transportgovuk road stats #opendata cc @puntofisso pic.twitter.com/rGhDinkaVX— John Murray (@MurrayData) May 28, 2019

.@jQAssistant is a #QA tool which allows definition & validation of project specific rules on a structural level. Built upon #Neo4j #graphdatabase, can be plugged into build process. Now w/ #PlantUML class diagrams #dataviz #softwareengineering

#W3CGraphWorkshop @b2ebs’s Keynote- Neptune seems to be favorite amazon product launch of 2018. People love graphs- “Graph let’s me integrate data like crazy”- View market as customers who could benefit from graphs- Devs from RDB find PG natural. Info arch find RDF natural pic.twitter.com/6tJDy0FKDz— Juan Sequeda (@juansequeda) March 4, 2019

#W3CGraphWorkshop Alastair Green @neo4j: - It’s always hard to agree. It’s a social process!- PG has to get organized. A lot going on: openCypher, PGQL, SQL/PGQ, G-CORE. RDF seems to be more organized. - Cooperate to define reasonable interoperation standards. pic.twitter.com/uEpZg4Jyd4— Juan Sequeda (@juansequeda) March 4, 2019

Happy to share what the Property Graph Schema Working Group has been working on for a few months. Slides https://t.co/7TzSMPobnqIndustry Survey https://t.co/MdfK1fL2wiUse Case & Requirements https://t.co/4ZuCy6zT6ZAcademic Survey https://t.co/J7rZYioIHG #W3CGraphWorkshop— Juan Sequeda (@juansequeda) March 4, 2019

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December 14, 2018 Posted by László Velinszky | Comments JUGRI: The JUpyter - GRemlin Interface...

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“How powerful are Graph Neural Networks? Slides from my talk at ITA workshop in San Diego. https://t.co/K4syDPYe82”

Just published a new version of neovis.js, a JavaScript graph visualization package designed to be used with @neo4j Graph Algorithms🎉This version includes support for TypeScript and a bugfix impacting some Angular issues. Kudos to Shoval for the PRs!https://t.co/1lXCZhgx1r pic.twitter.com/IsdLZxlhKX— William Lyon (@lyonwj) October 4, 2019

Visualize Kafka Streams with Neo4j by taking any data, turning it into a graph, leveraging graph processing, and piping the results back to Apache Kafka, adding visualizations to your event streaming applications.

KBpedia combines 6 knowledge bases (Wikipedia, Wikidata, schema.org, DBpedia, GeoNames, OpenCyc), has mappings to more vocabularies. Can be used for #machinelearning, #data interoperability, knowledge-based #AI #Newrelease #knowledgegraph #opendata [LINK]http://www.mkbergman.com/2284/kbpedia-continues-quality-improvements/[/LINK] [IMAGE]http://www.mkbergman.com/wp-content/themes/ai3v2/images/2017Posts/kbpedia-logo-244.png[/IMAGE]

The baseline KBpedia version 2.00, a computable knowledge structure that integrates seven leading public knowledge bases, has now been released as open source.

Discover how graphs are everywhere with the second installment of this series from Joe Depeau as he takes to graphing the complex world of tea.

Hi Marko and welcome back! Been a bit heads down - thanks for the link. Will go have a read.— Kelvin Lawrence (@gfxman) February 27, 2019

“I just published the first update of Practical Gremlin in all formats since July. This release adds quite a bit of content and resolves many issues. Thanks as always for all the feedback and support. https://t.co/UNJzfsUg3s @apachetinkerpop #graphdb”

In part 1 of this article series, I provided a quick primer on graph data structure, acknowledged that there are several graph based algorithms with the notabl…

#Knowledgegraphs are important resources for many #artificialintelligence tasks but often suffer from incompleteness. #research proposes to use pre-trained language models for knowledge graph completion. Novel framework named KG-BERT

Hi everyone, The Knowledge Graph Conference 2020 agenda is now live! Check it out. May 4-5 Workshops & Tutorials 2 workshops and 7 tutorials on building the state of the art KGs Schedule May 6-7 Main Conference 30 speakers of KG pioneers and leaders sharing vision for KG innovations Schedule KGC 2020 Tickets Join over 130 […]

The quality assurance of the knowledge graph is a prerequisite for various

.@DDIAlliance releases XKOS v1.2 (Extended Knowledge Organization System) specification →facilitates sharing & management of statistical classifications.(For building #Semantic Taxonomies to tag Datasets & #DataScience outputs)https://t.co/HZD1nl5Nu0#BigData #Ontologies #RDF pic.twitter.com/DDYbHqCvm7— Kirk Borne (@KirkDBorne) June 21, 2019

Bringing #graphdatabases technology and advanced analytics together. We at Redfield are proud to announce a new integration between #OrientDB graph database...

Image taken from wikipedia, Couple of days ago I was presenting “How to use similarity algorithms” in a Neo4j online meetup with Mark Needham. Among other use-cases we discussed how the…

Oct 25, 2019 · 11 min readThere is an unreasonable amount of information that can be extracted from what people publicly say on the internet. At Heuritech we use this information to better understand what people want, which products they like and why. This post explains from a scientific point of view what is Knowledge extraction and details a few recent methods on how to do it.What is knowledge extraction?Highly structured databases make it easy to reason with and can be used for inference. For example in WikiData or YAGO, entities are isolated and linked together with relations. However, most of the human knowledge expressions take the form of unstructured texts, from which it is very hard to reason and get wisdom. Consider the example here:The raw text on the left contains a lot of useful information in an unstructured way, such as birthday, nationality, activity. Extracting those information corresponds to a challenging field in Natural Language Processing, which may require

Prototyping a simple knowledge graph application with JSONs, MongoDB and automatically generated GraphQL API.Continue reading on The Startup »

Prototyping a simple knowledge graph application with JSONs, MongoDB and automatically generated GraphQL APIThis post is a result of joint work with Anna Konieczna and Artur Haczek.Source: Noble ConnectionsSimple knowledge graph applications can be easily built using JSON data managed entirely via a GraphQL layer. We describe a quick recipe for prototyping one such demo, Noble…

Knowledge graph applications can be technically challenging and time-consuming to build, but enterprises are starting to find that the work can lead to positive returns.