How Google may build and improve upon the Google knowledge Graph using knowledge graph reconciliation to remove ambiguity and add new entities

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

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. […]

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?

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

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

Graphs #Analytics for Fraud Detection, using Graph #NeuralNetworks for Anomaly detection. GraphSAGE is Stanford #opensource project: deep neural network-based NRL toolkit, implemented in Tensorflow, making it ideal to develop an anomaly detection system

AI has a critical role to play in fighting COVID-19 - @SemanticScholar has taken the lead in partnership with several groups to produce the COVID-19 Open Research Dataset (CORD-19), a free resource of 29K+ scholarly articles for the #AI research community: https://t.co/Pk94dvW6Kp— Allen Institute for AI (@allen_ai) March 16, 2020

With the huge volume of research now available, and the ability to manage and analyze enormous amounts of data, you could say there has never been a better time to be a researcher. The opportunities to build on each other’s work are greater than ever.However, all that information brings challenges, with researchers indicating that just navigating all that information takes more time than ever.So how about a system of information that could give researchers precise answers to their questions immediately, rather than having to browse a list of webpages that might hold the answer deep in the text?Researchers at Elsevier's DiscoveryLab – part of the Innovation Center for Artificial Intelligence (ICAI) in Amsterdam – are building a system to deliver just that. Dr. Frank van Harmelen, Professor of Computer Science at the Vrije Universiteit (VU) Amsterdam and Academic Director of Elsevier's DiscoveryLab, explained:What we’re developing is an integrated knowledge graph for resea

Managing multiple truths in #datamodeling, #ontologies & #knowledgegraphs by @palexop #semantics #vagueness #datascience #dataengineering #EmergingTech #dmzone #presentation

Gain a basic understanding of graph-based meta data management in enterprise data governance with Apache Atlas as a prime example.

This article takes a look at insight into evaluating a graph database gained from benchmarking different graph databases. Also explore loading capabilities.

Enables Users to Experience the World’s Fastest Graph Analytics Platform Designed to Unleash the Power of Interconnected Data for Deeper Insights and Better Outcomes

We often have questions like “What time is the meeting with Susan?” or “When does Walmart open?”. These questions ask for a property of an…

Watch the GraphConnect presentation by Dom Davis, cofounder of Tech Marionette, to discover the benefits of building a meta graph model for your domain.

Discover Neo4j Bloom, the latest product from the Neo4j team. This graph data visualization tool helps traditional Neo4j users communicate with their non-technical peers in a simple manner that reveals and explains the concepts of data connectedness for all people, regardless of technical background.

Discover a new way to visualize and explore your connected data with SemSpect: a unique approach to graph visualization that doesn't depend on using random or best-guess Cypher queries in order to explore your data's meta-graph and that is compatible with Neo4j (including RDF datasets).

Discover why your organization is a knowledge graph is essentail to build a competitive advantage with a graph dabase and machine learning algorithms.

Neo4j tunes its graph engine for AI applications - SiliconANGLE

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

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 ...