Neo4j tunes its graph engine for AI applications - SiliconANGLE

Connected Data London community cast. Michael Grove: Knowledge Graphs and Graph Databases have reached unprecedented levels of interest thanks to the efforts...

For the milestone of the 3.4 release, we’ve implemented a set of information retrieval features exposed via new database object View.

Recently similarity algorithms were introduced in Neo4j graph algorithms library, so I decided to show how easy it has become to infer a graph using Jaccard similarity and then run Community detect…

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

This case study shares how Refinitiv used ArangoDB to build a sophisticated Business Intelligence Platform.

TigerGraph has brought forward a free Developer Edition of its graph analytics platform for lifetime non-commercial use. But, please, what is graph analytics? As defined nicely here by Hitachi ...

SAN FRANCISCO, May 2, 2018 -- Neo4j, the leading platform for connected data, today announced Neo4j 3.4, which includes horizontal scaling, performance

Neo4j, DataStax Graph and Janus property graph schema design decisions for vertex and edge definitions.

Graph analytics is a hot topic, but what does it mean? At the DC GraphTour, I learned the difference between graph queries, graph algorithms, and graph analytics. Next up: San Francisco GraphTour.

AI is in its infancy and cannot be left unattended. Whether unintentional or by malicious design, algorithms behaving unexpectedly are now a fact of life.

Bye Bye Silos! Those who stay on their islands will fall back. This statement is valid on nearly any level of our increasingly complex society, ranging from whole cultural areas down to single individuals.

Stardog 5.1 adds support for GraphQL, an expressive path query SPARQL extension, and stored functions.

Learn how to find similar items in the Knowledge Graph with machine learning.

A knowledge graph is the only realistic way to manage enterprise data in full generality, at scale, in a world where connectedness is everything.

by Dr. Xu Yu, CEO and Dr. Victor Lee, Director of Product Management [Excerpted from the eBook Native Parallel Graphs: The Next Generation of Graph Database for Real-Time Deep Link Analytics] Until recently, graph database designs fulfilled some but not all of the graph analytics...

Recently RedisGraph published a blog [1], comparing their performance to that of TigerGraph’s, following the tests [2] in TigerGraph’s benchmark report [3], which requires solid performance on 3-hop, 6-hop, and even 10-hop queries. Multi-hop queries on large data sets are the future of graph analytics....

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

Five diverse technology companies show how it's done.

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

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

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

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.

.@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

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…

n

#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