GraphNews

FOOTBALL MEETS GRAPHS Bea Hernández

For those of you who missed it - here is the recording of my webinar at @Dataversity on #graph #datamodeling - summary of my #online #training at #Dataversity: https://t.co/3p71i3kGYP— Thomas Frisendal (@VizDataModeler) January 11, 2020

László Barabási never bothered to learn English. “My worst grades were always in English because I thought, Why study it? You can never leave this country,” explains Barabási, director of the Center for Complex Network Research (CCNR) at Northeastern University in Boston. “It wasn’t until I got to the University of Bucharest and became interested in research that I understood the importance of being able to read academic papers in English.” Barabási emigrated from Romania to Budapest with his father in the summer of 1989, a few months before Ceaușescu was overthrown, and completed a master’s degree in physics at Eötvös Loránd University two years later. But it wasn’t until after he’d earned a Ph.D. in physics at Boston University in 1994, while working as a postdoc at IBM’s legendary Thomas J. Watson Research Center, that Barabási became inter

Solutions Review highlights the most common graph database use cases you need to know about so you can select the best software.

In this blog post, we explore how and why semantics and graph data models are necessary when implementing a data fabric architecture.

Short comparision of #SPARQL and #GraphQL by @RubenVerborgh. One SPARQL Query can go against multiple SPARQL endpoints in a natural way - that is not equal to schema stitching in GraphQL because there is no need for schema assumptions of different #API's: https://t.co/RWYuvBSIqX— Francis Opoku (@fraopo) December 23, 2018

A big new #LOAD resource: https://t.co/J44j8EKsGo All 500M+ owl:sameAs statements in the #LOD cloud, their closure into 50M equivalence classes, and an estimated error degree for all of these. All for download at https://t.co/sKeetxXzgY— Frank van Harmelen (@FrankVanHarmele) April 28, 2019

Combines graph and document technologies, ontology design, and AI data science expertise

Grab your free copy of the brand new O'Reilly book, "Graph Algorithms: Practical Examples in Apache Spark & Neo4j" – a practical guide to graph analytics.

I believe that within five years there will be a dramatic growth in a new field called Knowledge Science. Knowledge scientists will be ten…

Who, what, when, where, how of anything and everything dressed in metadata with Margaret Warren of ImageSnippets and ... and a Porsche art car.

How AI is evolving towards custom graph hardware

From a bird eye's view, the DBpedia Extraction Framework takes a MediaWiki dump as input, and turns it into a knowledge graph. In this talk, I discuss the crea…

Generating structured #KnowledgeGraphs is relevant for decision making & information augmentation. #research studies generating KGs as relational representation of inputs: nodes represent entities, edges represent relations #EmergingTech #AI h/t @aaranged

The mission of FKG group is to create specifications for encoding ontologies that AI Assistants can operate upon enabling them to execute functions embedded in a web page. FKGs are encoded in JSON-LD, this group defines the vocabulary. A detailed proposal can be found at: https://github.com/keyvan-m-sadeghi/assister/blob/assister-conception/rfcs/text/assister-conception/README.md

Augmented analytics, continuous intelligence and explainable artificial intelligence (AI) are among the top trends in data and analytics technology that have significant disruptive potential over the next three to five years, according to Gartner, Inc.

Gartner Names PoolParty as Visionary in Metadata Management Magic Quadrant. Our modern approach combining machine learning and knowledge graphs makes the difference!

Published on February 22, 2020 3:28 AM GMT

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Joshua Shinavier from Uber coming up with a broad definition of knowledge graphs #us2ts2019 pic.twitter.com/0LbMMJQP6S— Gaurav Vaidya (@mrvaidya) March 11, 2019

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.

passing neural networks (MPNNs) have been successfully applied to

So you have heard the hype about knowledge graphs? How can Pythonistas join forces with Fashionistas to form an authoritative single source of truth for fashion e-commerce, benefiting applications such as Search, Discovery, and Personalisation. This talk will give you a unique insight into how knowledge graphs can provide powerful ways to analyse and emphasise relationships in data. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analys...

Do you want to learn more about #knowledgegraphs and data-centric architecture? @AlanMorrison put together a list of resources #datacentric #dataarchitecture #semantics #LinkedData #GraphDatabase

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