GUN is a distributed, offline-first, realtime graph database engine with built-in encryption.

For the past year I have been doing a deep dive into when to apply graph technologies in a large fortune 10 healthcare company. I have…

My good friend Ed Sverdlin often begins our “Introduction to Graph Databases” presentations with the question “When did computing start?”…

AI is fundamentally very simple but diving deep to understand it can be complicated. My aim is to make it merely complex and to understand…

This document provides an overview of two modeling strategies/patterns used for building knowledge graphs and triplestores of core biological ‘knowledge’ (e.g. relations between genes, chemi…

By treating reifications, assertions about assertions, as first-class objects in the Semantic Web, the idea of interchange between property and semantic graphs and the ability to work with graphs using a much broader set of tools opens up.

The distinctions between machine learning and semantics are disappearing - they are both simply tools for managing the metadata associated with the data that flows through every organization and domain.

How graph databases can be used to explore and analyse the decentralised XRP ledger, focusing on exploring networks of fraudulent accounts and distribution patterns.

Entity Extractions from Text on Web pages, instead of being limited to knowledge bases, may be used by Google to build knowledge graphs

This past fall, all aspects of the computable knowledge structure KBpedia – its upper ontology (KKO), full knowledge graph, mappings to major leading knowledge bases, and 70 logical concept groupings called typologies – became open source.

We explore the use of a knowledge graphs, that capture general or commonsense knowledge, to augment the information extracted from images by the state-of-the-art methods for image captioning. The...

Trace the safety information throughout the entire drug development lifecycle in a single intelligent system with Ontotext's solution.

As a follow up of word embedding post, we will discuss the models on learning contextualized word vectors, as well as the new trend in large unsupervised pre-trained language models which have achieved amazing SOTA results on a variety of language tasks.

The baseline KBpedia version 2.00, a computable knowledge structure that integrates seven leading public knowledge bases, has now been released as open source.

Boettiger, (2019). Ecological Metadata as Linked Data. Journal of Open Source Software, 4(34), 1276, https://doi.org/10.21105/joss.01276

LinkedIn has been forced to ‘pause’ a feature in Europe in which the platform emails members’ connections when they’ve been ‘mentioned in the news’. This follows a number of data protection complaints after LinkedIn’s algorithms incorrectly matched members to news articles — triggering an internal review of the feature. LinkedIn told us it subsequently decided […]

Augmented analytics tools, NLP search and graph analytics are the top trends for 2019 and beyond. Experts say augmented analytics and NLP are transforming how enterprises consume data and derive in...

If we wish to foresee the future of mathematics, our proper course is to study the history and present condition of the science. Henri…

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.

Graphs (not charts and pretty pictures) are an abstraction that was first used by Euler in 1736 to solve the now famous Konigsberg problem. This mathematical abstraction has been proven to be useful in several niche domains where complex networks (graphs are also known as networks) had to be analyze

Today I'd like to share an idea. It's a very simple idea. It's not fancy and it's certainly not new. In fact, I'm sure many of you have thought about it already. But if you haven't—and even if you have!—I hope you'll take a few minutes to enjoy it with me. Here's the idea: Every matrix corresponds to a graph. So simple! But we can get a lot of mileage out of it.To start, I'll be a little more precise: every matrix corresponds to a weighted bipartite graph. By "graph" I mean a collection of vertices (dots) and edges; by "bipartite" I mean that the dots come in two different types/colors; by ...

With our semantic chatbot, semantize your data without even having to understand what data semantization is!

Analysing Load and Query Times across DBPedia and Yago

#W3CGraphWorkshop @b2ebs’s Keynote- Neptune seems to be favorite amazon product launch of 2018. People love graphs- “Graph let’s me integrate data like crazy”- View market as customers who could benefit from graphs- Devs from RDB find PG natural. Info arch find RDF natural pic.twitter.com/6tJDy0FKDz— Juan Sequeda (@juansequeda) March 4, 2019