GraphNews

accessibility@cornell.edu for assistance.

world domain. A cursory Google search will result in

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…

This article provides a walk around the KBpedia knowledge graph and structure -- how to use it, what it does, and how to learn more about it.

In your opinion which structured data markup syntax is the easiest to use when adding #schema.org information to a web page?— Aaron Bradley (@aaranged) January 29, 2019

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Modeling_EN.md(Also available in French, by replacing EN by FR at the end of the URL)Trying to make a shorter answerAnd then I decided to make him a synthesis about the role of each feature …… and while writing this synthesis, I suddenly realized that these 3 features were all in connection with a problem of decompartmentalization !In fact, modeling paradigms often tend to compartmentalize the "things" ...... by

I would like to point to a crowd-funding of 📊 @rawgraphs, a project I find super interesting for a while and now they want to rewrite it. I think SPARQL and RAWGraphs can become best friends and so do they so help them getting there! https://t.co/VI0gK0ZZIS pic.twitter.com/UszINmFyuw— Adrian Gschwend (@linkedktk) October 2, 2019

Swiss EPFL's Blue Brain Nexus Project @bluebrainnexus released Nexus v1.0, which is from what I can see among others a SPARQL endpoint. I did not play with it yet but sounds like something to check out! https://t.co/y30L5thM44— Adrian Gschwend (@linkedktk) April 3, 2019

consuming because experts are required to pick out biomedical inte

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The Ontologies Community of Practice is engaged in the development of ontologies for agricultural research. In a series of blog posts, we’ll take a look at ongoing ontologies projects and developments.

#Knowledgegraphs are a natural fit for knowledge management: they model domains to retain more context & meaning even as information is parsed and abstracted for digital representation. Information is modeled in a way that is more intuitive & useful

Read the first installment of this blog series on artificial intelligence on the ways graph technology adds necessary context for powerful AI solutions.

Causal reasoning is a necessary ingredient to human-level artificial intelligence. We're not there, yet.

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Alan Morrison's answer: Graph databases are often used to store knowledge graph data and the accompanying description, predicate and rule-based logic. Knowledge graph: A knowledge graph is a knowledge base that’s made machine readable with the help of logically consistent, linked graphs that tog...

#knowledgegraph-based #research to design a 20 questions game on Bollywood movies. Uses probabilistic learning model for template-based question generation & answer prediction. Dataset of interrelated entities represented as weighted knowledge graph #AI

An interactive map of all the world's disputed areas from #wikidata. Built in 10 minutes by @planemad, using SPARQL #dataviz #knowledgegraph #opendata #graphDB #data #tech #EmergingTech #datascience Map #visualization on left-side menu h/t @LearningSPARQL [LINK]https://t.co/a4Yngspwds[/LINK] [IMAGE]https://pbs.twimg.com/tweet_video_thumb/EKBb_NGW4AIYhEH.jpg[/IMAGE]

Heritage institutions are places in which works of art, historical records, and other objects of cultural or scientific interest are sheltered and made accessible to the public. The equivalent of that in the digital world, is already taking shape, through digitization and sharing of digital-born or

An Introduction to Graph Theory by @mpvenables Before diving in, we need to understand #data structure & networks in #machinelearning. Networks are useful for #apps, from driving directions to social networks #datascience #tutorial #analytics #AI [LINK]https://towardsdatascience.com/an-introduction-to-graph-theory-24b41746fabe[/LINK] [IMAGE]https://miro.medium.com/max/480/1*rnZ3FbSvWMVvcRP78fXeCg.png[/IMAGE]

Neural Networks aimed at effectively handling graph data.Photo by Alina Grubnyak on UnsplashGraph structured data is common across various domains, examples such as molecules, { social, citation, road } networks, are just a few of the vast array of data which can be represented with a graphs. With the advancements of machine learning we witness the potential for applying intelligent algorithms on the data which is available. Graph Neural Network is the branch of Machine Learning which concerns on building neural networks for graph data in the most effective manner.Notwithstanding the progress made with ML in the computer vision domain with convolutional networks, Graph Neural Networks (GNNs) face a more challenging problem, they deal with the awkward nature of graphs. Differently from images and text, graphs do not have a well defined structure. A graph’s node might have no connections or many, of which could be directed or undirected. Graphs in a dataset may have a variable

Etsy has an uncontrolled inventory; unlike many marketplaces, we offer an unlimited array of one-of-a-kind items, rather than a defined set of uniform goods. Etsy sellers are free to list any polic…

This guide will help you understand JSON-LD and structured data markup. Go beyond the online generators and prepare your web pages for the future of search!

Analyzing US Lobbying Data in #Neo4J #graphdatabase

https://t.co/7T0EOs6yG7 - Made this tiny tool to discover related subreddits.The graph is created based on jaccard similarity between two subreddits. Jaccard similarity is constructed from set of shared users.Source code https://t.co/J9r1jl1JjR pic.twitter.com/4hcg7mI4sg— Andrei Kashcha (@anvaka) January 10, 2019

.@CamSemantics announces Anzo #GraphDB v.2: RDF*, Custom SDK, Free Edition. "Imagine being able to do labeled properties, just like you do in Neo4j & other property graphs, but also have capability of RDF to help w ontologies & inferencing" #data #tech

powered applications with minimum friction.Broad compatibility with existing and future software developm