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

Introduction

It’s no longer necessary to choose between RDF & LPG #knowledgegraphs. RDF* offers the best of both worlds so you don’t have to give up anything. #AI #EmergingTech #data #analytics #datascience

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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.

Using Redis #NoSQL #graph capabilities to build and query a family tree. #softwaredevelopment #tutorial #graphdatabase #data #datamodel #apps #code [LINK]http://gigi.nullneuron.net/gigilabs/family-tree-with-redisgraph/[/LINK] [IMAGE]http://gigi.nullneuron.net/gigilabs/wp-content/uploads/2019/11/redisgraph-family-tree-example-1024x494.png[/IMAGE]

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.

Xiangrui Meng of Databricks recently posted this on the Apache Spark project users list: http://apache-spark-user-list.1001560.n3.nabble.com/SPIP-DataFrame-based-Property-Graphs-Cypher-Queries-and-Algorithms-td34358.html Databricks and Neo4j contributors are looking to bring Cypher queries into the core Spark project as part of Spark 3.0 (slated for release mid-year 2019). This will build on elements from the Cypher for Apache Spark and Graphframes projects. All the details are in the links i...

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.

Computerworld covers a range of technology topics, with a focus on these core areas of IT: Windows, Mobile, Apple/enterprise, Office and productivity suites, collaboration, web browsers and blockchain, as well as relevant information about companies such as Microsoft, Apple and Google.

Finding the shortest path from Country A to Country B using Graph #analytics #algorithms #javascript #tutorial #softwareengineering #datascience #GraphDB #opensource #Neo4J #data #tech [LINK]https://technology.amis.nl/2019/01/01/finding-the-shortest-path-from-country-a-to-country-b-using-neo4j-and-node/ [LINK]https://miro.medium.com/max/1370/0*Ol4T6m9f4Y2W7T9W.png

If you want to use GraphQL with Neo4j from any JVM based API, this library helps you to use your schema to transpile the query to Cypher.

Solutions Review highlights the most common graph database use cases you need to know about so you can select the best software.

In this blog post, we explore how and why semantics and graph data models are necessary when implementing a data fabric architecture.

Combines graph and document technologies, ontology design, and AI data science expertise

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.

From a bird eye's view, the DBpedia Extraction Framework takes a MediaWiki dump as input, and turns it into a knowledge graph. In this talk, I discuss the crea…

The mission of FKG group is to create specifications for encoding ontologies that AI Assistants can operate upon enabling them to execute functions embedded in a web page. FKGs are encoded in JSON-LD, this group defines the vocabulary. A detailed proposal can be found at: https://github.com/keyvan-m-sadeghi/assister/blob/assister-conception/rfcs/text/assister-conception/README.md

We all want to know “Where does our meat come from?” or “Is this a reliable information or fake news?”. If we ask this kind of questions…

Even with all the buzz around the technology and the snazzy visualisations, graph databases are still a novel concept for many organisations. My recent experience of working with company register relationship data and evaluating graph databases from various vendors led to some interesting insights i

Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org - Accenture/AmpliGraph

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

Graph+Semantics: Import/Export RDF from Neo4j. SHACL Validation, Model mapping and more.... If you like it, please ★ ⇧ - neo4j-labs/neosemantics

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

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