Concur. This is a great, detailed resource for those using #schema.org/Dataset https://t.co/j8H4ZUff2O pic.twitter.com/25KmdwOEJr— Aaron Bradley (@aaranged) December 8, 2020

Actually, this is pretty interesting > Ontology-based and User-focused Automatic Text Summarization (OATS): Using COVID-19 Risk Factors as an Example https://t.co/YvvevnKtbV pic.twitter.com/5njn4ZXeKj— Aaron Bradley (@aaranged) December 8, 2020

A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources https://t.co/JsMcHN8Z6g pic.twitter.com/IxbRmK4ioo— Aaron Bradley (@aaranged) December 8, 2020

"... we argue that to date there are no effective solutions for supporting developers' decision-making process when deciding on an ontology reuse strategy" > The Landscape of Ontology Reuse Approaches @vale_carriero et al. https://t.co/LTqn85wv26 pic.twitter.com/AwjVPUDYk7— Aaron Bradley (@aaranged) December 8, 2020

Towards a Shared Peer-Review Taxonomy: An interview with Joris van Rossum and Lois Jones https://t.co/VQ0C807Rms pic.twitter.com/wGls0T8QKC— cbaumle (@cbaumle) December 10, 2020

"We present ten simple rules that support converting a legacy vocabulary - a list of terms available in a print-based glossary or table not accessible using web standards - into a FAIR vocabulary." https://t.co/5Jby0iXAXU pic.twitter.com/c2ovJjiAd5— Aaron Bradley (@aaranged) December 10, 2020

"https://t.co/u85cjjArgc’s primary focus is people—individuals who were enslaved, owned slaves, or participated in slave trading."Data curated in RDF, you can browse the resources and get RDF out of it. I could not find a SPARQL endpoint yet https://t.co/jwjDtYnhsD— Adrian Gschwend (@linkedktk) December 11, 2020

As requested , here are a few non-exhaustive resources I'd recommend for getting started with Graph Neural Nets (GNNs), depending on what flavour of learning suits you best. Covering blogs, talks, deep-dives, feeds, data, repositories, books and university courses! A thread 👇 pic.twitter.com/el1kb8rS4G— Petar Veličković (@PetarV_93) September 17, 2020

From Knowledge Graphs to Knowledge Categories. @joshsh interviews Ryan Wisnesky of @ConexusAI for @TheGraphShow https://t.co/b1dxpdrBoh #knowledgegraphs #categorytheory #RDF #tinkerpop pic.twitter.com/wUpILFIV5z— Graph Day (@GraphDay) November 18, 2020

How can we gain maximum utility from a sophisticated ontology engineering such as #FIBO? @kptyson tells us how - by applying property paths combined w/ reasoning. Such techniques are useful for quality assuring large, complex ontologies & #knowledgegraphs. https://t.co/ZSfOVj5M1k— Ontotext (@ontotext) November 17, 2020

In this publication, Amazon scientists present the COVID-19 Knowledge Graph (CKG), a heterogeneous graph for extracting and visualizing complex relationships between COVID-19 scientific articles. Learn more: https://t.co/gmLEXJo48m #AmazonScience #COVID19 #KnowledgeGraphs pic.twitter.com/CzugJUhjs3— Amazon Science (@AmazonScience) November 16, 2020

"Ontology-driven Event Type Classification in Images" exploit structured information from #Wikidata to learn relevant event relations using deep neural networks. (Müller-Budack et al, 2020)https://t.co/fUFEN6wBqI@sherzodhakimov pic.twitter.com/CKyMMOKMaW— WikiResearch (@WikiResearch) November 19, 2020

Here's my first blog post for Amazon Neptune which discusses the new features it supports with its recent inclusion of @apachetinkerpop 3.4.8. #graphdb https://t.co/ImzR2rFIfX pic.twitter.com/EeHOR5k2xF— stephen mallette (@spmallette) November 18, 2020

"Fact-checking via Path Embedding and Aggregation", over Wikidata and DBPedia.(Pirro', 2020)https://t.co/opnd01It06 pic.twitter.com/b1IN7WomSA— WikiResearch (@WikiResearch) November 23, 2020

I say for some years that graph scaling is solved by throwing enough hardware at it and I'm VERY excited to hear we might even get dedicated "graph" hardware for it in the future! Great explanation! https://t.co/6XoAI3VOD2— Adrian Gschwend (@linkedktk) November 23, 2020

Leading enterprise #knowledgegraph provider @StardogHQ announces Stardog Cloud industry's first #cloud native offering enables organizations to transform enterprise data infrastructure into a comprehensive end-to-end #datafabric— Yuri Simione (@artika4biz) November 22, 2020

Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction https://t.co/hxdwBmvCKV pic.twitter.com/M2oOmtcB7G— Aaron Bradley (@aaranged) November 25, 2020

"Linking OpenStreetMap with Knowledge Graphs - Link Discovery for Schema-Agnostic Volunteered Geographic Information", using Wikidata and DBPedia.(Tempelmeier and Demidova, 2020)https://t.co/VAxB6fsxEb pic.twitter.com/OwKtzY9C59— WikiResearch (@WikiResearch) November 25, 2020

👉 bbw is our new semantic annotator matching tabular data with #Wikidata knowledge graph via meta-lookup.It is Open Source https://t.co/VaTZHoaodH and comes with reproducible binder links https://t.co/VwJzkh4Og7 resp. https://t.co/ynlXEl1awM. Try it out! pic.twitter.com/ZoedYVPJUM— Philipp Zumstein (@zuphilip) November 24, 2020

"... there’s another step beyond that which is business modelling, and that needs to be represented in a knowledge graph. Knowledge graphs are how we’ll do predictive analytics in the 2030s." - former @SnowflakeDB CEO @bob_mugliahttps://t.co/lQaAvhuA4Q #knowledgegraph— Juan Sequeda (@juansequeda) November 30, 2020

For graph database users, you have now an open source jupyter notebook you can use with many different graph databases, as long as they are compatible with RDF SPARQL or with Apache TInkerpop, including Amazon Neptune https://t.co/e3EODiXb6a— javier ramirez (@supercoco9) November 26, 2020

"Introducing Inter-Relatedness between #Wikipedia Articles in Explicit Semantic Analysis", enhancing articles' vector representations with information about relations between Wikipedia pages.(Elango and Prasad K, 2020)https://t.co/Hb7rHIrIyj#NLProc@iitmadras pic.twitter.com/xLt89t1IBh— WikiResearch (@WikiResearch) December 3, 2020

In this talk, there are a few thoughts on Semantic MediaWiki as a generic "thinking tool", as a pay-as-you-go development environment, or, as I call it, the #ultimatenerdsnipe I don't think I expressed these ideas in a recorded talk before, so I hope you enjoy it! https://t.co/XTfwCFLa2B— Denny Vrandečić (@vrandezo) December 4, 2020

Guide to graph representation of data and how to perform feature extraction from graphs using DeepWalk. Learn about DeepWalk and its python implementation

For the last few years, I have been promoting the idea of the Hardware Graph. My assertion was that graph hardware needs a focus on simple…

Network analysis offers enormous potential for journalism: able to tease out controversial connections and curious clusters, and to make visible that which we could not otherwise see, it’s al…

Great explanation of #LinkedData and #SPARQL with Buckets and Ballsby @kvistgaard https://t.co/wBPZWgNmUG pic.twitter.com/QOEA5BG7di— Elena Makurochkina (@elenamdata) October 26, 2020