Is there life after COVID-19? Of course there is, even though it may be quite different, and it may be hard to get there. But there’s one thing in common in the “before” and “after” pictures: science and technology as the cornerstones of modern society, for better or worse.

We have argued before that Knowledge Graph is a technology that enables other technologies to accelerate their growth, and it also enables humans to take stock of their own knowledge. This is why the future is Knowledge Graph.

Today’s most pressing issue seems to be dealing with the COVID-19 pandemic. Science is taking on that task, too, and technology is there to facilitate it. Knowledge Graphs facilitate scientific research and business decision making, as they have been doing, and will continue to do.

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Producing results to fight against the SARS-CoV-2 (coronavirus) is one of the most pressing issues today, bringing the entire scientific community together. Addressing issues related to scientific research can help produce results under pressure.

Here is how prominent researchers Alexander Jarasch and Sören Auer use the state of the art in analytics and AI, graphs analytics and knowledge graphs, to facilitate scientific research for the COVID-19 pandemic. For an in-depth live discussion, join us in this Connected Data London online Meetup on April the 22nd.

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There is a wide range of datasets and applications released lately centered around the coronavirus. Aaron Bradley has collected a good part of those in a Twitter thread. Add to those a couple of startup initiatives, from Tellic and Kahun

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Life goes on, however. Research and development, product releases, and new features never stop. Case in point – we’ve seen a few significant updates from graph database vendors in the last couple of months.

Graph databases and analytics are getting ever more accessible and relevant, and vendors seem to be going through a virtuous circle of innovation. DataStax has unified graph with native Cassandra data model in DSE 6.8, TigerGraph 3.0 brings no code data migration and visual querying, and Neo4j has released a BI connector and Neo4j for Graph Data Science.

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Even though the Graph technology landscape is shifting rapidly all the time, it’s good to get a snapshot every once in a while. This is Janos Szendi-Varga’s 2020 update of his Graph technology landscape overview.

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Another hot topic we’ve been keeping an eye on is graph machine learning. A wealth of relevant information is out there. From an introduction to deep learning for graphs and where it may be heading, to a roundup of knowledge graphs interwoven with AI in AAAI 2020. From making graph machine learning scalable, to an outlook of AI, Machine Learning and the Future of Graphs.

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How about some real world knowledge graph use cases? Joshua Fruhlinger built a knowledge graph of knowledge graphs, so to speak. Plus, Walmart’s knowledge graph.

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On a different note: here is how to build your own knowledge graph, using your own data as Facebook stores them. And for some more fun distraction, here’s a Kardashian knowledge graph.

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As knowledge graphs are attracting attention beyond their historical user base, educational resources are in high demand. Teodora Petkova has written a guide for people getting started in this domain.

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If you’d rather attend an online class, Stanford has got you covered. A series of lectures is on offer, featuring top knowledge graph experts. It’s online, it’s free, and it’s recorded, too.

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If you’re more of a textbook kind of person, then this book by C. Maria Keet may be your thing.

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For the more hands-on group, here is how to use Wikipedia to build a knowledge graph, and automate the process as much as possible.

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Wrapping up the knowledge graph educational section for the academically inclined. This publication has as many authors as it has angles. In its 130 pages it covers everything from history to future research directions for knowledge graphs. A propos, there is another deep dive in the history of knowledge graphs.

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What about lowering the barrier to entry for software engineers? That seems to be the rationale behind accessing knowledge graphs via GraphQL, and it makes sense. Given GraphQL’s popularity, it’s not surprising that many people have had a go at this.

From 15 minute hands-on tutorials to long reads on the roots of GraphQL and the synergies with graph databases, and everything in between.

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Speaking of software development: a couple of new open source frameworks have been added to a growing list. Zazuko has published a tutorial on getting started with RDF & JavaScript using open source libraries, and AtomGraph has released what it dubs “the WordPress of graph data”.

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Wrapping up with a roundup of research coming from the likes of Salesforce and Stanford, plus open source graph embeddings: RDF2Vec for Python, and as a Web API.