We applaud today’s decision by the American Chemical Society to endorse @DORAssessment and to make citation data for all their journals openly available. One more major publisher supporting the vision of unrestricted access to scholarly citation data 🚀 https://t.co/VNMwefVEbX— I4OC (@i4oc_org) February 18, 2021

@neo4j's @maya_natarajan discusses "Why #KnowledgeGraphs now?" #BBBT pic.twitter.com/DsRSKL97O1— Adrianna Dyczkowsky (@ADyczkowsky) February 19, 2021

If you're interested in GNNs for combinatorial tasks (certainly an exciting time!), we've released our 43-page comprehensive survey on the area! + detailed blueprint of algorithmic reasoning in S3.3.https://t.co/F4TG4svKMGwith @chrsmrrs @69alodi @lyeskhalil @qcappart & Didier pic.twitter.com/P6TANTgLvr— Petar Veličković (@PetarV_93) February 19, 2021

We’re thrilled to share a proposal to extend the work of the #WikiCite community, to support Wikimedia Projects and make citations: • easier for the editor,• more useful for the reader,• and more efficient for our architecture.#SharedCitationshttps://t.co/pqVwQLApNa 1/ pic.twitter.com/oALIPg7JTD— WikiCite (@Wikicite) February 22, 2021

Accurate cardinality estimates are a key ingredient to achieve optimal query plans. For RDF engines, specifically under common knowledge graph processing workloads, the lack of schema, correlated...

Graph theory has a long history of problems being solved by amateur mathematicians. Do you want to try yourself to become one of them?

"The Open Application Group (OAGi) and the Industrial Ontologies Foundry (IOF) have signed an agreement to produce industrial ontologies." [PR] https://t.co/zz511UBuYE— Aaron Bradley (@aaranged) March 3, 2021

Identifying Ingredient Substitutions Using a Knowledge Graph of Food https://t.co/5pZWc9Bt94 pic.twitter.com/PgkMr9idHj— Aaron Bradley (@aaranged) March 3, 2021

Basic Introduction to what Graph Analytics is…

new life sciences SPARQL endpoint. License unclear, and contains (with permisison) some proprietary data (MeSH): https://t.co/HuvlSNhRo3 https://t.co/gSFH7o7Oxo— Egon Willighⓐgen (@egonwillighagen) February 25, 2021

Interactively Constructing Knowledge Graphs from Messy User-Generated Spreadsheets / Markus Schröder et al. @DFKI https://t.co/XQ6ZNbOm6z pic.twitter.com/lkMnLGka4L— Aaron Bradley (@aaranged) March 8, 2021

https://t.co/ooIuC1elTy version 12 is out - thanks to all who collaborated on this! https://t.co/rCzFkiwbBo has the details. In this edition https://t.co/ooIuC1elTy can now distinguish 6 kind of media-authenticity problem for reviewing images and videos; ... pic.twitter.com/a6yZv0ayDR— Dan Brickley (@danbri) March 8, 2021

It gives a framework and place to begin, recognizing that no outreach plan will work for everyone so it’s necessarily an iterative, step-by-step process.

New @orkg_org release today featuring a new contribution editor, where you can directly add a comparison table to capture the state-of-the-art for a particular research problem: https://t.co/nhZ7Gjzilh pic.twitter.com/MJmLp9bvIi— Sören Auer (@SoerenAuer) March 8, 2021

Graph Networks are extremely useful tools to help understand the graph data that's all around us. In this episode, I'm going to explain what some of the latest advancements in graph networks are and how you can leverage them to build your own graph network in a few lines of Python code. This space has matured so much that there is never a single library to discuss, there are always multiple competing options (which is a great thing). We'll be weighing the pros and cons of the Deep Graph, Graph Nets, and PyTorch Geometric library as well. I'm particularly interested in Graph Networks because...

I have written a lot on how to create good taxonomies. But what's the case for having taxonomies in the first place? I excerpted 21 introductory slides...

"Birds of a feather flock together"

There is a lot of mystique surrounding knowledge graph that it can sometimes be daunting to approach graph technology. I am here to let you in on a little secret, that you don't need to worry about all the jargon and the tech behind them to understand fundamentally what graph is and why people are talking about it. Check out the video to learn more (and take part in the giveaway)! Stay in touch: LinkedIn: https://www.linkedin.com/in/ashleighnfaith/ Direct Message: isadatathing-at-gmail.com Resources: Relational compared to graph: https://neo4j.com/developer/graph-db-vs-rdbms/ Graph database...

Although artificial intelligence capabilities are improving daily, it is not always easy to put the AI rubber on the road – especially when it comes to understanding AI’s contextual data and problem-solving approaches. How about bringing in some “real” intelligence? Graphs are a typically human way

Ever wish it was just easier to learn about taxonomy? Or how about teaching the Girl Scouts about data science? Or maybe finally getting your friends to understand what you actually do at work? Join me in this step by step walk through on how to make a taxonomy and how you can use your junk-drawer to help your teams, family, and friends learn with you. No jargon, no strict rules, just having fun while we play with data. Kit Materials: (I got mine from the Dollar Tree but any odds and ends will do) 1 pack of colored pencils 1 pack colored markers 2 packs "pompoms" 1 pack beads of the same sh...

Ever wish it was just easier to learn about ontology and knowledge models? Or better yet, how to use a taxonomy to BUILD an ontology? Or how about teaching the Girl Scouts about data science? Or maybe finally getting your friends to understand what you actually do at work? Join me in this step by step walk through on how to take a taxonomy and make a pizza ontology to help your teams, family, and friends learn with you. No jargon, no strict rules, just having fun while we play with data. Part 1: Making the taxonomy https://youtu.be/bLNWyfc2jvQ OWL file for the original Stanford Pizza Ontolo...

So much to explore in this thesis by @nickvosk, much of which is focused on "making structured knowledge more accessible to the user by describing and contextualizing [knowledge graph] facts" > Supporting search engines with knowledge and context https://t.co/sIQjQY3zri pic.twitter.com/C89v4YyrO5— Aaron Bradley (@aaranged) February 17, 2021

A Google patent describes how Google may read pages on the Web to build a large data graph that can be used to answer queries.

Learning Intents behind Interactions with Knowledge Graph for Recommendation https://t.co/ayEfZtdBMu pic.twitter.com/B6w5MKAnJz— Aaron Bradley (@aaranged) February 21, 2021

3/5 [Demo]: Neuro-Symbolic Question Answering: A KBQA system that enables use of neuro-symbolic reasoner by leveraging semantic parsing. SoTA on two DBpedia based KBQA datasets Paper: https://t.co/4p0Sy4vlGwBlog: https://t.co/mYIXuH8dbA@ibrahimabdelazz @sroukos— Pavan Kapanipathi (@pavankaps) February 6, 2021

Linked Data projects at the Vrije Universiteit Amsterdam Network Institute @dbpedia https://t.co/uYNpEMGn0Y— Aaron Bradley (@aaranged) February 9, 2021

Over 28,000 peer-reviewed scientific papers about graph-powered data science have been published in recent years - Read more here!

Patterns for Representing Knowledge Graphs to Communicate Situational Knowledge of Service Robots https://t.co/xktHqliz8q pic.twitter.com/yZgiSFgQeI— Aaron Bradley (@aaranged) January 28, 2021

The English WordNet in #Neo4j. https://t.co/buXxA8OdqX #NLP #KnowledgeGraphs pic.twitter.com/MY0EAaRDon— Graphs & Networks (@TheOrbifold) February 1, 2021

A Knowledge Graph for the Agri-Food Sector @rapw3k https://t.co/Uko5uhcg2F— Aaron Bradley (@aaranged) February 1, 2021