We are going to show how we import the W3C Organizational Ontology into Neo4j using the neosematics tools.

like experience for quickly importing CSV data into Stardog. To get started, just choose a database under Studio’s”Databases”tab and click on”ImportCSV.” Studio’s wizard will extract the headers (if any) from the CSV file you supply and will let you choose both a name for the class of data that the CSV represents(i.e.,the type of thing to which each row of the CSV corresponds) and the column that should be used for generating unique identifiers for instances of that class. To help you choose a truly unique identifier, the wizard will also show you just how distinct the data in each column of the CSV is, and will indicate whether or not the column you’ve chosen is likely to be a good one with respect to data integrity. Data Exploration

Learn about how AstraZeneca visualized patient journeys, answered important questions about prescriptions and diagnoses, and improved patient outcomes.

Five diverse technology companies show how it's done.

.@TDataScience talks about inference on #SemanticWeb and how to apply in a local #graphDB. What is Inference? What is it used for? Types of the procedure, Graph #Database & #Ontology, Inference in a Database #knowledgegraph #semantics #tutorial

Discover how the big data company Thomson Reuters used Neo4j to develop a real-time intelligent graph recommendation engine for financial analysts.

To understand trends as they’re happening, @pinteresteng needs to understand content & categories. To do that, they built a taxonomy-based knowledge management system that enables content understanding in a highly efficient way #knowledgegraph #AI #data

Amazon Neptune is a fast, reliable, fully managed graph database, optimized for storing and querying highly connected data. It is ideal for online applications that rely on navigating and leveraging connections in their data. Amazon Neptune supports W3C RDF graphs that can be queried using the SPARQL query language. It also supports Apache TinkerPop property […]

Until now the only way to run Graph Algorithms on Neo4j has been to learn Cypher. The Graph Algorithms Playground changes all that.

Introducing the Neo4j Graph Data Science plugin with examples from the “Graph Algorithms: Practical Examples in Apache Spark and Neo4j” bookIn the past couple of years, the field of data science has gained much traction. It has become an essential part of business and academic research. Combined with the increasing popularity of graphs and graph databases, folks at Neo4j decided to release a Graph Data Science (GDS) plugin. It is the successor of the Graph Algorithms plugin, that is to be deprecated.Those of you who are familiar with Graph Algorithms plugin will notice that the syntax hasn’t changed much to allow for a smoother transition. To show what has changed, I have prepared the migration guides in the form of Apache Zeppelin notebooks that can be found on GitHub.Neo4j connector for Apache Zeppelin was developed by Andrea Santurbano, who also designed the beautiful home page notebook of this project and helped with his ideas. In the migrations guides, we used the ex

3DSculptor/Getty Images Every big company now manages a proliferation of sites, apps, and technology systems for interacting with buyers and managing everything in the business, from customers and clients to inventory and products. These systems are spitting out data continuously. But even after multiple generations of investments and billions of dollars of digital transformations, organizations struggle to use that data to improve customer service, reduce costs, and speed the core processes that provide competitive advantage. AI was supposed to help with that. But as an executive at a major life insurance company recently told me (Seth), “Every one of our competitors and most of the organizations of our size in other industries have spent at least a few million dollars on failed AI initiatives.” Why? My 20 years of experience working with companies on their information technology have shown me the reason: because promises of AI vendors don’t pay off unless a company’

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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

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.

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.

.@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

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…

There are only a handful of publicly available knowledge graphs. And among those, only a few provide data with enough breadth to in some way represent the entire internet, and with enough granulari…

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#KnowledgeGraphs and their central role in #bigdata processing: Past, Present, and Future #keynote #presentation #research #AI #datascience #data @amit_p

19, trying to make sense of the data surrounding the virus is a Herculean task. Vast in volume and ceaselessly produced, this data emanates from domains as different as virology and economics and is produced by a multitude of people and organizations. Unsurprisingly the standards to which this data conforms are as multitudinous as its sources. It just so happens that making sense of messy data from disparate sources is one of the things at which knowledge graphs excel. Moreover, knowledge graphs make it possible to derive new knowledge from intelligently connecting information residing in those disparate data repositories. Given that the ability to better analyze data and gain new insights is of obvious use to people trying to respond to the pandemic, those working with knowledge graph technologies have started to talk about how those technologies – and their skills – might

At Stardog, we’re all about making it easy to unify data. That’s why we’ve just open sourced a set of tools to make working with Stardog even easier than before.

Dependencies, like graphs, are everywhere. Achieving a goal is rarely possible in a vacuum and requires collaboration between individuals and/or process...

This is a summary of a short talk I gave internally at the ODI to help illustrate some of the important aspects of data standards for non-technical folk. I thought I’d write it up here too, i…

GQL is an effort to standardize property #GraphDB query language. GQL project lead leaving #Neo4j, WG3 is now responsible. "There is no certainty about the success of the GQL project, but there are good objective and subjective grounds for optimism"

#smartcity stakeholders must build #datagovernance & management ready for all categories of #data. Required:assessment of expected current/future categories, scalable, flexible, modular design. Shared, unified, standards-based graph data model: #ontology