"... the first version of Meena reportedly has 2.6 billion parameters and is trained on 341 GB of text, filtered from public domain social media conversations." @jrdothoughts bit.ly/2UXJHYF

A "definitive feature of knowledge graphs is the ability to connect concepts to external resources using Linked Data". ✔️ @taxobob bit.ly/2SDis4h

If you’re one of the holdouts who still has a Facebook account you’ll be happy to know that Big Zuck now lets you control — and delete —…Continue reading on Medium »

The growing interest in making use of Knowledge Graphs for developing

SourceThe year 2020 has just started but we can already see the trends of Graph Machine Learning (GML) in the latest research papers. Below is my view on what will be important in 2020 for GML and the discussion of these papers.The goal of this article is not on introducing the basic concepts of GML such as graph neural networks (GNNs), but on…

This article shows how an RDF Graph CRUD application can be rapidly developed, yet without losing the flexibility that HTML5/JavaScript offers, from which it can be concluded that there is no reason preventing the use of RDF Graphs as the backend for production-capable applications.

At linked data-driven companies you will find it difficult to distinguish between “traditional” data workers and those in other functional areas who, at other companies, are less reliant on data.

Overview of the research domain and works of the joint research team Wimmics (UCS, Inria, CNRS, I3S) in Sophia Antipolis, France

Graph machine learning finds dissatisfied customer cohorts an recommends optimal intervention measures.

Prelude In December, I wrote a Quora post on the pros and cons of graph databases. I shared two cons pervasive in the market today: the difficulty of finding proficient graph developers, and how non-standardization on a graph query language is slowing down enterprise adoption,...

Massively parallel implementation of #Graph2Vec = scalable graph representation #algorithm learns vectors that describe whole graphs in an embedding space:https://t.co/nR0Iv2QG4u by @benrozemberczki#BigData #DataScience #AI #MachineLearning #LinkedData #GraphDB #GraphAnalytics pic.twitter.com/7INPwfeDPw— Kirk Borne (@KirkDBorne) August 25, 2019

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).

Machine learning algorithms don’t yet understand things the way humans do — with sometimes disastrous consequences.

Learn how to use Neo4j to integrate a BigQuery public dataset with Stackdriver logs into a graph database, to surface new conclusions from complex data.

An evaluator's task is to connect the dots between program goals and its outcomes. This can be accomplished through surveys, research, and interviews, and is frequently performed post hoc. Research evaluation is hampered by a lack of data that clearly connect a research program with its outcomes and, in particular, by ambiguity about who has participated in the program and what contributions they have made. Manually making these connections is very labor-intensive, and algorithmic matching introduces errors and assumptions that can distort results. In this paper, we discuss the use of ident...

Making decentralized Web app development fun ◆ While the Semantic Web community was fighting its own internal battles, we failed to gain traction with the people who build apps that are actually used: front-end developers. Ironically, Semantic Web enthusiasts have failed to focus on the Web; whereas our technologies are delivering results in specialized back-end systems, the promised intelligent end-user apps are not being created…

I just published the latest version of Practical Gremlin in all supported formats. Another substantial update. Please see the change history for full details. https://t.co/UNJzfsUg3s … … https://t.co/mXGaOEe3q6 … … https://t.co/7YtyD2xQuR … … @apachetinkerpop @JanusGraph pic.twitter.com/EcokGbuwMN— Kelvin Lawrence (@gfxman) May 29, 2018

What is an application network? And why does it need a graph? MuleSoft's founder and CTO help explain today's announcement at Dreamforce 2018

We show how to create an embedding to predict product reviews, using the TensorFlow machine learning framework and the Neo4j graph…

We demonstrate connecting a Neo4j graph database to Keras. We build a neural network achieving 100% test accuracy on a simple review…

We’re pleased to announce the start of a multi-part series of posts for Amazon Neptune in which we explore graph application datasets and queries drawn from many different domains and problem spaces. Amazon Neptune is a fast and reliable, fully-managed graph database, optimized for storing and querying highly connected data. It is ideal for online […]

Many graph applications can benefit from the ability to capture changes to items stored in an Amazon Neptune database, at the point in time when such changes occur. Amazon Neptune now supports Neptune Streams, a fully managed feature of Neptune that reliably logs every change to your graph as it happens, in the order that […]

Graphs are a general-purpose #datamodel, as relational was a general-purpose #data model a generation ago. A supply chain is a graph. Knowledge is a graph. Graphs are very applicable in a wide range of use cases @jimwebber @TheRegister #GraphDB #tech [LINK]https://www.theregister.co.uk/2020/02/05/graph_database_neo4j_chief_scientist/ [LINK]https://regmedia.co.uk/2016/04/26/graph_database.jpg

Optimizing #algorithms on large scale labeled property graphs. Comparing optimization approaches, directly querying #graphdatabase. Aim: determine limiting factors of #GraphDBs. Speedup of a factor between 44 & 3839 #knowledgegraph #research #EmergingTech [LINK]https://arxiv.org/abs/2002.03686 [LINK]https://i.imgur.com/75Wxc5b.png