GraphNews

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

known technique in machine learning consisting in representing complex objects like texts, images or graphs into a vector with a reduced number of features (~100) compared to the dimension of the dataset (several billions nodes in a graph for instance), while keeping the most important information about them.It is quite easy to understand when thinking about words, when the word meaning has to be preserved by the encoding. It is often represented by this equation:Queen = King — Man + Womanwhich basically says that, in the embedding space, the representation of the word Queen must be equal to the vector representation of King minus Man plus the representation of Woman. It is also represented on the next schema.Sutor, Peter & Aloimonos, Yiannis & Fermüller, Cornelia & Summers Stay, Douglas. (2019). Metaconcepts: Isolating Context in Word Embeddings. 544–549. 10.1109/MIPR.2019

By the mid 2000s, it was clear that the vision of the semantic web, as set out by Tim Berners Lee in 2001, despite a huge amount of initial hype and investment, remained largely unrealized. The basic idea — that a network of machine readable, semantically rich documents would form a ‘semantic web’ much like the world wide web of documents, was proving much more difficult than had been anticipated. This prompted the emergence of the concept of ‘linked data’, again promoted by Tim Berners Lee, and laid out in two documents published in 2006 and 2009.Linked data was, in essence, an attempt to reduce the idea of the semantic web down to a small number of simple principles, which were meant to capture the core of the semantic web without being overly prescriptive over the details.The 4 basic principles were:

TigerGraph 2.4 is available now and introduces powerful additions that make it even easier to integrate TigerGraph into your existing infrastructure, get your data into TigerGraph, and get simpler and faster results.

The lack of support for stateful cloud-native application behavior is a roadblock to many cloud use-cases. This article looks at graph knowledge-based systems which offer one approach to the design of next-generation platforms.

Graph #MachineLearning meets #UX; an uncharted love affair. @nhungphnguyen explores machine learning on graphs, then builds end-to-end story to define users, their relationships & different needs #productdesign #uidesign #AI #EmergingTech @Data61news

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

Graph Neural Networks (GNNs) are information processing architectures for

Many real-world tasks require understanding interactions between a set of entities. Examples include interacting atoms in chemical molecules, people in social networks and even syntactic interactions between tokens in program source code. Graph structured data types are a natural representation for such systems, and several architectures have been proposed for applying deep learning methods to these structured objects. I will give an overview of the research directions inside Microsoft that have explored different architectures and applications for deep learning on graph structured data. Se...

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...

limitless scalability.RDF and the Semantic web, w

Few years ago I decided that one day I would create a Graph Technology Landscape map, which would be useful for everyone who wants to discover the playe...

Sep 4 · 4 min read As a part of writing a book with Dr. Matthias Broecheler, I spent time talking to dozens of graph users from around the world.

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Moving data between semantic and property graphs

When developing web applications with frameworks like Vue.js the best approach is to subdivide it into well-defined and reusable components for the user...

Graphileon joins CovidGraph, an initiative of graph enthusiasts and companies with the goal to build a knowledge graph with relevant information about the COVID-19 virus.

time recommendations.

time recommendations.

(Submitted on 21 Feb 2020) Abstract: Knowledge graphs have proven to be extremely useful in powering diverse

Graphcore CEO Nigel Toon discusses his company's computer processor and opportunities to disrupt the computer chip industry. He speaks with Bloomberg's Caroline Hyde on the sidelines of Bloomberg's Sooner Than You Think conference in London. (Source: Bloomberg)

Ontotext announces the release of GraphDB 9.0, open-sourcing GraphDB front-end and engine plugins to empower the developers of knowledge graph solutions

Ontotext releases GraphDB 9.2 featuring the anticipated support for RDF*/SPARQL* and improvements in the plug-ins for semantic similarity and versioning.

GraphDB 9.3: optimized support for arbitrary path length in SPARQL brings quicker discovery of relationships in knowledge graphs

Much of what Graph databases revolve around deals with classification. This alone can be quite challenging - learn more on the G DATA TechBlog!

2) from worldwide labs, the Graphen team, in conjunction with Columbia University, is able to align the genome of viruses, look for the canonical form of each gene location, and identify the exact variant(s) of a virus. Each virus has nearly 30k bases with each position represented by one of ATCG, the cDNA of virus. A virtual Canonical form sequence was determined independently in each position, not based on a single virus. In the Canonical form, the genomic length of a virus is 29,816. The letter of each position of the Canonical form was independently determined by the consensus of all sequenced viruses. Genome sequences need to be first aligned. The max position number after alignment is 30,532 which includes some head/tail/empty holes. After the canonical form is available, for each virus, we can then identify its exact variation in each position by comparing to the Canonical f

working individuals working overtime delivering groceries, people are taking on a role in supporting our society when we need it the most.These selfless people inspire us and we want them to know that we’re in this together. It’s time for us as a community to collaborate and do something positive.So — we invite you to join us and the global development community in an effort to unite our skills and bring some good to our world. ❤❤Let’s hack for good, together.Image source: CNN MoneyAbout Graphs4Good (GraphHack) 2020WHAT: any project that has a positive goal and can help others, qualifies. ❤WHERE: virtually, of course!