GraphNews

Comparing #GraphDatabases by reading @G2dotcom #reviews. Reasonable as a 1st approach, #reviews are just one #datasource to be used for such a compex decision. Take them with a grain of salt, use your own experience/education/benchmarks, consult experts

Analysing Load and Query Times across DBPedia and Yago

wide crawlers can crawl the exact same pages and pull out extensively different data.

The index to the multiple installments in the 'Cooking with Python and KBpedia' (CWPK) series.

This post will showcase the use of cosine similarity algorithm in Neo4J and also provide examples in addition to the available…

We're currently building a Data Governance Platform product that enables UK Financial Services organisations to discover and manage the life-cycle, usage, risk and compliance requirements of data asse

Read this blog to learn more about the benefits of using a native graph database to build a real-time recommendation engine with personalization.

Creating Graphs in Python using Networkx #dataviz #tutorial #datascience #data #tech

How might we model graphs beyond the most common use cases, and take a more creative approach to graph modeling?

Knowledge graphs have emerged as a compelling abstraction for organizing world's structured knowledge over the internet, capturing relationships among key entities of interest to enterprises, and a way to integrate information extracted from multiple data sources. Knowledge graphs have also started to play a central role in machine learning and natural language processing as a method to incorporate world knowledge, as a target knowledge representation for extracted knowledge, and for explaining what is being learned. This class is a graduate level research seminar featuring prominent researchers and industry practitioners working on different aspects of knowledge graphs. It will showcase how latest research in AI, database systems and HCI is coming together in integrated intelligent systems centered around knowledge graphs.The seminar will be offered over Zoom as per the planned schedule.The seminar is open to public. Remote participants may join the seminar through Zoom. To be

In this post I explain common issues with query performance in Neo4j and how you may solve them using the Query Log Analyser tool.

.@cytoscape is an #opensource #software platform for complex networks #dataviz. #Neo4j Graphs are often too large for Cytoscape; this plugin allows you to write Cypher queries, import result as a network. Queries can be parameterized & stored for reuse

The German automotive giant is using Neo4j to help its HR team better understand its constantly changing corporate structure and reporting lines at a time when employees increasingly work in ‘swarms’

In this contributed article, editorial consultant Jelani Harper discusses how data modeling is the foundation of the data science discipline that’s [...]

In the third installment of Database Deep Dives, we caught up with JanusGraph PMC members Florian Hockmann and Jason Plurad to get some guidance on the wide world of Graph.

DataStax Enterprise 6.8 fulfills the promise of integrating the graph engine and is another step in the road toward realigning the flagship platform with the Apache community.

Increasingly complex products undoubtedly require greater management of components, function and data. Classic product data management (PDM) has long reach its limits in this respect. Breaking down product DNA is now driven by artificial intelligence (AI) and knowledge graphs. In… Read more →

Discover tips and strategies for implementing graph embedding into a Neo4j graph database, with plenty of Cypher examples.

This post made it to top 10 on HackerNews front page. Do engage in discussion

infection, repeated use of the public places and transportation, the spreading path of the virus becomes a network structure. Thus a

Criminals are constantly finding new and more sophisticated ways to commit fraud. Every technological development presents new opportunities for illicit activities, and few more so than the evolution of digital currencies.

Within a growing organization, finding the right information can take up a good part of your day.

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