"Pixie is one of Pinterest’s major recommendation systems used for fetching relevant Pins. Pixie is composed of a bipartite graph of all Pins and boards on Pinterest." https://t.co/Ng5PszF07x— Aaron Bradley (@aaranged) September 11, 2020

"In RDF, properties cannot be directly associated with edges. How would we represent something like [an LPG labeled edge] in RDF? In fact there are multiple ways of modeling this. A common approach is reification." @chrismungall https://t.co/0MQxFomSvX— Aaron Bradley (@aaranged) September 11, 2020

GeoSPARQL+: Syntax, Semantics and System for Integrated Querying of Graph, Raster and Vector Data - Technical Report / @situxxx, @ststaab, Daniel Janke https://t.co/qrsPikb9U0 pic.twitter.com/dQ7qdzP26O— Aaron Bradley (@aaranged) September 11, 2020

Using Graph Convolutional Networks and TD(λ) to play the game of Risk https://t.co/Js0tUctECl pic.twitter.com/9EgI7hZa3d— Aaron Bradley (@aaranged) September 15, 2020

Speaking of personalized knowledge graphs....Knowledge Graphs to Empower Humanity-inspired AI Systems @hemant_pt, Valerie Shalin, @amit_p https://t.co/7gYiAxNHhU pic.twitter.com/bVccm8IxCK— Aaron Bradley (@aaranged) September 16, 2020

Tax Knowledge Graph for a Smarter and More Personalized TurboTax @jiebingyu et al. of @Intuit https://t.co/XZP9afAU34 pic.twitter.com/iYAcCVmmVn— Aaron Bradley (@aaranged) September 15, 2020

@davidebus is delivering a brilliant keynote at DeepOntoNLP (https://t.co/XYE99QzEgB) about of role of #NLP and #DeepLearning in the generation of the #Artificial #Intelligence #KnowledgeGraph (AI-KG, https://t.co/rH7MgH7e89) from research publications. pic.twitter.com/SkWhpx6IUg— Francesco Osborne (@FraOsborne) September 16, 2020

Traditional KGs are based on triples, whereas new KGs like #wikidata use statements and qualifiers to instantiate each edge further making the graph hyper-relational (img1). We incorporate these qualifiers by modifying existing multi-relational GNN (CompGCN) in the StarE (img 2). pic.twitter.com/Qs2xYZEDQy— Michael Galkin (@michael_galkin) September 15, 2020

#kdd2020 Very excited to share our work on Continuous-time Graph Neural Network: "Neural Dynamics on Complex Networks" soon in the KDD2020 research track with @feiwang03 @WCMPopHealthSci @WeillCornell @kdd_news Arxiv: https://t.co/nfaLr94aPkA learned network dynamics of genes pic.twitter.com/I9FwP7wkqz— Chengxi Zang (@calvin_zcx) August 27, 2020

A new post coauthored with Kirill Veselkov and Gabriella Sbordone on the use of #graphml for discovering #cancer-beating molecules in #food and the concept of #hyperfoods based on our Scientific Reports paper @KitchenTheory @Jozef_Youssef @VodafoneFdn https://t.co/rsZzMH8kpK pic.twitter.com/OJBgiqfrDV— Michael Bronstein (@mmbronstein) September 15, 2020

CODO: An Ontology for Collection and Analysis of Covid-19 Data https://t.co/qKt4WJowzE pic.twitter.com/rTsdqJrqh1— Aaron Bradley (@aaranged) September 14, 2020

"How might a web application work if it exclusively uses Linked Data (RDF) to communicate between server and client?" @joepmeindertsma https://t.co/BJa13ERMo7— Aaron Bradley (@aaranged) September 14, 2020

It was a pleasure to present "Challenges of Linking Organizational Information in Open Government Data to Knowledge Graphs" (joint work w/ @OmaimaAF @sebneum @iamYaserJ @AxelPolleres) today at #EKAW :) Preprint: https://t.co/Q7ot16bRMa#BOSK #EKAW2020 #knowledgegraphs pic.twitter.com/oYYVoC7fVk— Jan Portisch (@JanPortisch) September 18, 2020

Type-augmented Relation Prediction in Knowledge Graphs https://t.co/aKqGI7S3GW pic.twitter.com/2PgPhEVvTJ— Aaron Bradley (@aaranged) September 18, 2020

This "Load balance graph queries using the Amazon Neptune Gremlin Client" blog post is a nice body of work covering a more advanced topic than is typically seen in the TinkerPop community. https://t.co/4mJVRxENwL #graphdb pic.twitter.com/d94tOwUaQB— stephen mallette (@spmallette) September 17, 2020

This year we organize Graph ML track at Data Fest 2020. It's like a workshop at the conference, but more informal. We will have videos from amazing speakers and also networking, where you can talk to me, speakers, or other people who are interested in graph machine learning. pic.twitter.com/kTrJsc7ca2— Sergey Ivanov (@SergeyI49013776) September 17, 2020

Industry Applications of Network Science and Graph Algorithms : from @Tamer_Khraisha https://t.co/i4RbHEVokk#networkscience #graphalgorithms #graphdatabase #graphtheory #graphdatabase pic.twitter.com/KktQ4GGmyc— Graph Day (@GraphDay) September 16, 2020

Knowledge Graphs and Big Data Processing : From @ValentinaJanev @hajiraajabeen @DGraux Emanuel Sallingerhttps://t.co/2hLlLXwZ1l#knowledgegraphs #bigdata #graphdatabase #graphdatabases #knowledgegraph pic.twitter.com/Mo46l3yj4p— Graph Day (@GraphDay) September 16, 2020

Here's a nice demo video with real-world examples of various simple graph analytics over an RDF-based knowledge graph that leverage the RDF*/SPARQL* approach, including a brief intro to knowledge graphs and RDF*/SPARQL*. https://t.co/xQKin9PA5w— Olaf Hartig (@olafhartig) September 13, 2020

Understand parallels between recent works on latent graph learning and older techniques of manifold learning @mmbronstein #GaphNeuralNetworks #ManifoldLearning https://t.co/IzgXseBNL3 pic.twitter.com/ygeUte7kKZ— Experfy (@Experfy) September 21, 2020

"Covid-on-the-Web: Knowledge Graph and Services toAdvance COVID-19 Research" a dataset comprising twomain knowledge graphs, including named entities linked to @DBpedia, @Wikidata and other @BioPortal vocabularies.(Michel et al, 2020)https://t.co/iZJH9Y2PpV pic.twitter.com/7hY0QpUQfv— WikiResearch (@WikiResearch) September 21, 2020

"To utilize document-centric XML data linked from entities in LOD, in this paper, a SPARQL-based seamless access method on RDF and XML data is proposed." > SPARQL with XQuery-based Filtering https://t.co/Jz0SrfLFA6 pic.twitter.com/rhsWPEXOyb— Aaron Bradley (@aaranged) September 15, 2020

The ICIJ used Neo4j to expose a vast money laundering network running through Western banks. This blog analyzes the FinCEN Files data.

The Research team at the @Wikimedia Foundation will give an overview on the first draft of the taxonomy of knowledge gaps in Wikimedia projects. You can read more about the taxonomy here: https://t.co/8eAj6mpvIP pic.twitter.com/P4p29vJO7i— WikiResearch (@WikiResearch) September 22, 2020

"What’s taking the world by storm today (...) is something very different. It is #bigdata and layers and layers of complex mathematics used to solve very specific(...) problems." -Andrea Malick. See how #knowledgegraphs are "adding the human factor" to #AI https://t.co/oQsGkJ00pQ pic.twitter.com/DlUP7vB2qH— Carbon LDP (@CarbonLDP) September 23, 2020

Use #gremlin to find the shortest path between nodes in a #graph. Checkout this Gremlify workspace:#graphdb #gremlinhttps://t.co/qYZb5EbaVL— gremlify.com (@gremlify) September 24, 2020

"#Neo4j announced a Knowledge Graph Quick Start program to support the company’s rapidly growing knowledge graph customer base."Learn more about the Knowledge Graph Quick Start program: https://t.co/Bnqo6O9hbW#knowledgegraphs #neo4j #graphtechnology— Neo4j (@neo4j) September 24, 2020

Community detection by label propagation using @graphileon and built-in #graphalgorithms in @Neo4j. A demo (https://t.co/zdY3wnc4YC), and why we think that #YourAppIsAGraph is the new way of building graphy applications. @nodejs #lowcoding. pic.twitter.com/YzBP3vCE2D— Graphileon (@graphileon) September 24, 2020

"RDF2Vec Light – A Lightweight Approach for Knowledge Graph Embeddings"(Portisch et al, 2020)paper: https://t.co/6kt4P3p74Oproject page: https://t.co/q1nXGR8jfc pic.twitter.com/11QxcOBFEI— WikiResearch (@WikiResearch) September 24, 2020

In the intricate play b/n #metadata, #ontologies & #textanalysis, each has an important role in capturing the multiplicity & complexity of data relationships. When we talk about #semantics & #knowledgegraphs, this is what we mean. https://t.co/0LznDdGry6— Ontotext (@ontotext) September 24, 2020