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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Albany, US, 2019-Jan-23 — /EPR Network/ —Market Research Hub (MRH) has actively included a new research study titled “Global Graph Database Market” Size

Read the latest installment of Joe Depeau's blog series on graphing and pathfinding the London Underground, complete with Cypher queries and data models.

Learn more about the Closeness Centrality graph database algorithm, which measures how a central a node is within its cluster.

Graph databases and analytics are getting ever more accessible and relevant

If you have been wondering what I've been up to lately you should take a look at the book I have been writing, "Graph Databases in Action" for Manning....

Property graphs provide a lot of flexibility in data modeling; but how do you know when to use which feature?

When building graph data models we frequently have to deal with a degree of polymorphism for our entities just like the real world. For instance – I’m a person, but I’m also a parent, a spouse, a sibling, a child, a… Implicit categorisation Sometimes the entity categories are entirely defined by relationships to other entities. […]

#Oracle offers #GraphDatabase, Graph #Analytics free of charge with purchase of main Oracle product license. 2 different products: Database built over Oracle's relational store using RDF/SPARQL. Analytics in-memory engine, custom query language

The graph database market is very exciting, as the long list of vendors continues to grow. You may not know that there are huge differences in the origin story of the dozens of graph databases on the market today. It’s this origin story that greatly impacts the superpowers and weaknesses of the various offerings.

Graph databases enable users to map the relationships between different data elements for analytics applications. In this handbook, we look at graph database use cases and provide real-world exampl...

Graph databases and document databases make up a subcategory of non-relational databases or NoSQL. NoSQL databases were created to get a handle on large amounts of messy Big Data, moving very quickly. Managers use the non-relational toolkit to gain business insights and detect patterns in information on the fly, as Big Data streams into the system. Many companies, especially those with a large web presence like Google, Facebook, and Twitter, consider NoSQL databases a must-have.

Graph databases are shaped the way we think, so why can't people get their heads around them?

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Look for semantic data systems to become more oriented around knowledge bases rather than inferential analysis, at least at first, while at the same time incorporating the latest advances in machine learning and auto-categorization.

How can companies step themselves into the world of graph databases? Neo4j thinks it has an answer. It has been offering a Startup Program for startups with 19 employees or fewer; more than 650 startups with fewer than 20 employees took advantage of having free access to Neo4j Enterprise clusters.

One of the challenges of Big Data is dealing with highly variable data. There are other challenges which we can recall by the other “V”s…

Continuing the analysis on semantics and data science, it’s time to talk about graph databases and what they have to offer us.

Neo4j’s Alicia Frame explains how life science researchers can exploit graph databases to get truly granular insight into big data to make major leaps forward in medical research.Complex data sets hold the key to advancing medical breakthroughs. These data sets tend to be voluminous and heterogeneous by nature, presenting an insurmountable challenge for traditional data analysis methods as they struggle to link patterns and outcomes. The unfortunate consequence is a slowdown in the progress of research.Anyone who works in life sciences is aware that they are working with highly connected information; the challenge is making sense of these connections. Unfortunately, many scientists are still using relational databases and spreadsheets which makes mapping important patterns and connections unintuitive and difficult, if not impossible.Graph technologyGraph technology is emerging as an enabler for researchers to trawl gargantuan amounts of unstructured data, turning it into valuab

A bit of history: the wild, early days of graph DBs Maybe not many know that billions of dollar/euros have been (publicly) invested from the early 2000 to 2015+ in a topic called "Semantic web" which had a kind of graph databases - triplestores - and it's related knowledge representation format (RDF

How to model #Facebook posts and user reactions, using #Neo4j #graphdatabase #socialmedia #data #analytics #softwareengineering #knowledgegraph

In this short technical blog, I will show you how to use GSQL to search a graph for all the occurrences of a small graph pattern. We call this pattern matching. Consider the problem of matching a pattern of vertices and directed edges in a...