New #openaccess research from @jjkoehorst and colleagues: A protocol for adding knowledge to #Wikidata: aligning resources on human coronaviruses. #Covid19 Read it here: https://t.co/TWkHhRPlA1 pic.twitter.com/ayoSFs0ztd— BMC Biology (@BMCBiology) January 25, 2021

Today’s most pressing data challenges center around connections, not just tabulating discrete data. The ability for graph data science…

"Semantic Models for Constructing #KnowledgeGraphs"My new article for @TDataScience, inspired by the original research of @caknoblock @szeke @JoseAmbite @taheriyan @dipsykapoor @USC_ISIhttps://t.co/hOZzAAP2usOther articles on a similar topic: https://t.co/GgiAPWQBFX pic.twitter.com/zemF3xAzDA— Giuseppe Futia (@giuseppe_futia) December 16, 2020

Nice table of of RDF*/SPARQL* implementations under “Preliminary implementations” at https://t.co/VhNSOcM93s— Learning SPARQL (@LearningSPARQL) December 17, 2020

“Wikipedia Knowledge Graph for Explainable AI” conference paper https://t.co/4SXkKKgDMK— Learning SPARQL (@LearningSPARQL) December 19, 2020

We kicked off our #NeurIPS2020 series joined by @TacoCohen, ML Researcher at @Qualcomm @Qualcomm_Tech, to discuss his current research in equivariant networks and video compression using generative models, as well as his paper “Natural Graph Networks.”— The TWIML AI Podcast (@twimlai) December 22, 2020

"Semantic Annotation for Tabular Data", using knowledge graphs like #DBPedia and #Wikidata.(Khurana and Galhotra, 2020)https://t.co/75kUf4lRvM pic.twitter.com/MUHpeMsGO0— WikiResearch (@WikiResearch) December 23, 2020

The Apache Rya team is happy to announce the release of Apache Rya 4.0.1, the *first* release as an ASF top-level project. Check it out at https://t.co/N58phhH6SO#RDF #BigData #TripleStore #SPARQL @ApacheAccumulo @MongoDB @ApacheFluo @TheASF— Apache Rya (@apacherya) January 5, 2021

Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective Lamb et al.: https://lnkd.in/dE3ZkMF #TrustworthyAI #KnowledgeRepresentation...

Semantic Linked Knowledge Web Data Graphs?

This is my third annual post on Enterprise Knowledge Graph (EKG) trends. You can also find my 2019 and 2020 posts on this blog, and I…

New Pizza Tutorial for Protege 5. Includes basic ontology development plus new sections for SWRL, SPARQL, and SHACL.

The purpose of a knowledge graph is to answer a user's questions. Some of the questions may be known upfront, while some questions users may never think of themselves.

Modelling complex relationships in RDF graphs

Top 5 SEO predictions for 2021: it's time to re-think your content marketing strategy while focusing on continuous innovation and AI.

We are proud to announce DBpedia Archivo - an augmented ontology archive and interface to implement FAIRer ontologies.

Your guide to the KG-related research in NLP, December edition

Knowledge graphs are giving companies new insights into their businesses and helping them create new services and improve R&D research.

"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

Background

We've made it even easier (and fun!) to explore the relationships between the millions of CC-licensed content sprawled across the web.

Ontotext and Synaptica join Forces with a Shared Product Roadmap to Speed up the Development of Enterprise Knowledge Graphs.

Upcoming at @emnlp2020:@danaikoutra and @tararootcake present CoDEx, a set of knowledge graph completion datasets extracted from @wikidata and @Wikipedia that improve upon existing knowledge graph completion benchmarks inscope & difficulty.https://t.co/YrZA4ac5GR— MichiganAI (@michigan_AI) November 13, 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

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