Just added >11M #OpenCitations to #COCI, for an overall amount of >733M citations currently available in our dataset – it can be queried via #REST API & #SPARQL endpoint and can be fully downloaded as a dump (available on #Figshare)+info at https://t.co/nxSlZGkb3G #OpenScience pic.twitter.com/FZhHYN782y— OpenCitations (@opencitations) September 7, 2020

Rule-Guided Graph Neural Networks for Recommender Systems https://t.co/Rt2TRzVllt pic.twitter.com/52A8tvKNz5— Aaron Bradley (@aaranged) September 10, 2020

"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

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

@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

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

"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

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

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

Cool! RDF* support in Neo4j's RDF plug in. https://t.co/f7t2Iyo8rY— Olaf Hartig (@olafhartig) September 24, 2020

“How do you select a graph database? Learn how in @itworldca. Read here: https://t.co/Z529PWdeW6 | #GraphDatabase #GraphDB #GraphAnalytics #DataScience #Developer #Analytics #BigData”

Finding buried treasure: article on the role of graph databases in HR & HCM (Human Capital Management) #KnowledgeGraphs#Datahttps://t.co/pZB7PuDco9 pic.twitter.com/mQqH2Drhqy— Synaptica LLC (@Synaptica) September 25, 2020

TopQuadrant CEO, Irene Polikoff, provides an overview of the two main graph models along with illustrations of their similarities and differences in graph diagrams in Part I of II in this article series from @TDAN_com https://t.co/CxOrTb3ELL#knowledgegraphs #datagovernance— TopQuadrant (@TopQuadrant) September 25, 2020

“My post on Mount Rainier’s Laplacian (https://t.co/ONdkZJdayX) is a 101 intro to aspects of spectral graph theory. This great talk by @mmbronstein shows how this theory also forms the basis of deep learning on graph-like structures. Read at least 5 papers today because of it. https://t.co/eMsV9...

I'll be discussing "Graph Queries with Gremlin Language Variants" at the Category Theory and Applications group meetup on October 6: https://t.co/MG1HpNEiGd Be prepared to see Gremlin in many different forms! #graphdb pic.twitter.com/OIOsfLvWze— stephen mallette (@spmallette) September 28, 2020

"Therefore, the job of data scientists is to decode the data and to find the knowledge encoded in the data—i.e., to find the model in the data, because the data is the model." - @KirkDBorne, @BoozAllen https://t.co/sZJzBkiA1p pic.twitter.com/NpKaDLLUuL— Kirk Borne (@KirkDBorne) September 28, 2020

😉Happy to share our work on multi-hop (open-domain) QA (https://t.co/7BhmCqX64m). TL;DR: you don't need the Wikipedia hyperlinks to achieve SoTA performance on HotpotQA(@qi2peng2 )! A shared RoBERTa encoder (for both Q and docs) is all you need to retrieve SP passages! 1/3 pic.twitter.com/B9JO85Vd1l— Wenhan Xiong (@xwhan_) September 29, 2020

Interesting paper on which parts of SPARQL query language are easier or more difficult to understand: https://t.co/TgWNXFrCfv— Learning SPARQL (@LearningSPARQL) September 29, 2020

Everything you always wanted to know but never dared to ask about #knowledgeGraphs:On Oct. 27, 2020, our new free online course "Knowledge Graphs" with @lysander07 and @em_alam will start on the @openHPI platform. Register now at https://t.co/UA9XxTOO3O pic.twitter.com/V6YGkV5Pu4— Harald Sack (@lysander07) September 12, 2020

Lots of very good material here. Covers a lot of ground including Amazon Neptune, performance tuning, data modeling, common use cases and also some @apachetinkerpop Gremlin and @w3c RDF tutorials. https://t.co/3gdcKcNHiT— Kelvin Lawrence (@gfxman) October 1, 2020

https://t.co/kSTvjW2oBv Such a thoughtful article on #knowledgegraphs! @TDataScience pic.twitter.com/BphubNoYwv— James Le (@le_james94) October 1, 2020

This current issue of https://t.co/iSEn2yvLfR covers #datagovernance #dataestate #data's #gendergap #datacatalog #dataestate #knowledgegraphs #DMP ... more. New content from @RSeiner @MandySeiner @AJAlgmin Polikoff of @TopQuadrant Beechum and @HBKI71. https://t.co/EEuVW3g7SG pic.twitter.com/sIL7KvLiJp— TDAN (@TDAN_com) October 1, 2020

In case you missed it, the recording of last week's #Lotico session on JSON-LD is now available. https://t.co/Pzlyjh2S89. #jsonld cc/@neumarcx— Gregg Kellogg (@Gkellogg) October 1, 2020