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

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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

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

Knowledge graphs are at the core of many of the tools that we use in our daily lives, such as voice assistants (Alexa, Siri or Google Assistant), intuitive search applications and even online store recommenders. Not only internet giants but also companies from other industries such as BBC, Capital One, Electronic Arts or AstraZeneca have already integrated the technology and are using knowledge graphs to harness the power of all of the data they have accumulated over the years.

answering service — among other things.Expanding a knowledge graph often involves integrating it with another knowledge graph. But different graphs may use different terms for the same entities, which can lead to errors and inconsistencies during integration. Hence the need for automated techniques of entity alignment, or determining which elements of different graphs refer to the same entities.In a paper accepted to the Web Conference, my colleagues and I describe a new entity alignment technique that factors in information about the graph in the vicinity of the entity name. In tests involving the in

The graph database market is very exciting, as the long list of vendors continues to grow. You may not know that there are huge differences in the origin story of the dozens of graph databases on the market today. It’s this origin story that greatly impacts the superpowers and weaknesses of the various offerings.