by Allard Oelen and Markus Stocker

What do eBay, Airbnb, Microsoft, Lending Club, and Comcast have in common? They're all using knowledge graphs to understand their customers, business decisions, and product lines. Why? Because they easily and intuitively visualize the nature, depth, and interdependence of the relationships they've created with their business decisions.

This article has two sections. The first describes the longstanding difficulties in integrating data for analytics and more recently, data science and AI.

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

19, trying to make sense of the data surrounding the virus is a Herculean task. Vast in volume and ceaselessly produced, this data emanates from domains as different as virology and economics and is produced by a multitude of people and organizations. Unsurprisingly the standards to which this data conforms are as multitudinous as its sources. It just so happens that making sense of messy data from disparate sources is one of the things at which knowledge graphs excel. Moreover, knowledge graphs make it possible to derive new knowledge from intelligently connecting information residing in those disparate data repositories. Given that the ability to better analyze data and gain new insights is of obvious use to people trying to respond to the pandemic, those working with knowledge graph technologies have started to talk about how those technologies – and their skills – might

(Submitted on 21 Feb 2020) Abstract: Knowledge graphs have proven to be extremely useful in powering diverse

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Big data combined with advances in data science approaches have provided a valuable opportunity for the pharma industry to uncover…

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GraphDB has generated a thriving community over the years and they share their insights and experience in many blog posts and tutorials.

risk people are those who have been within two meters for 15 minutes or more, with a known infected person in

type of CreativeWork: it is the domain of nine properties. Man

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on for experimenting with natural language models. The tech giant describes it as a demonstration of how natural language understanding (NLU) can be used with pretrained, generic AI models, as well as a means to dispel intimidation around using machine learning.

We are a diverse team of scientists, developers and data people from academia and industry.

In the previous part, we started to outline what ontologies are and how they may be used; now it is time to get into more practical guidelines and tips…

learning by itself is only half a solution.To explain this (and the relationship that graphs have to machine learning and AI), it's worth spending a bit of time exploring what exactly machine learning does, how it works. Machine learning isn't actually one particular algorithm or piece of software, but rather the use of statistical algorithms to analyze large amounts of data and from that construct a model that can, at a minimum, classify the data consistently. If it's done right, the reasoning goes, it should then be possible to use that model to classify new information so that it's consistent with what's already known.Many such system

It seems like every crime show includes a few scenes where we see that the detective has built a wall of pictures, newspaper clippings, index cards and other interesting documents, linking all these things together through a network of string and push pins. This linked data allows them to step back and see how the facts relate and helps provide the bigger picture of what happened and how to solve it. Seeing the bigger picture allows detectives to use inductive and deductive reasoning to pursue leads,identify gaps in their knowledge and continue the investigation in ways they may not have seen before.The crime wall is a physical representation of knowledge or context. The crime wall helps investigators see the relationships and understand the true meaning of the facts surrounding a case. Understanding context can lead to accelerated insights and increase the productivity of the detectives. In the digital world, we can represent knowledge through similar techniques. We call this di

related on Google. I was very excited when I discovered this latest enhancement as I believe it will benefit patients the most. In reality, most patients don’t know what type

accessibility@cornell.edu for assistance.

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based diagnosis of disease, and drug repurposing. Integrating data and knowledge is a formidable challenge in biomedical research. Although new scientific findings are being discovered at a rapid pace, a large proportion of that knowledge is either locked in data silos (where integration is hindered by differing nomenclature, data models, and lice

In case you have little to no experience with RDF you might want to read the RDF Primer first, which gives a good basic introduction to the concepts of RDF.

world domain. A cursory Google search will result in

prem or both. The discovery and integration layer in the fabric integrates and blends data, drawing on subsets of data from across the underlying data landscape as required.

Part 3 in a series about Knowledge Graphs

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bases and in addition, it includes a multitude of different international chapters/language communities.On the other hand, timbr DBpedia represents a synergy between DBpedia + SQL. Permits the querying of the DBpedia ontology/Open Knowledge Graph (OKG) via s