I've written a blog which shows you how to embed a #3D #MindMap with hyperlinks in your homepage https://t.co/Y1ZUUreAio , an example is available at https://t.co/l8ap24ZcMc pic.twitter.com/5QWesXLaFt— Ingo Straub (@inforapid) March 14, 2021
"The UI allows individuals with no previous knowledge of the Semantic Web to query the DBpedia knowledge base...." > Interface to Query and Visualise Definitions from a Knowledge Base @anelia12430996 & Hélène De Ribaupierre https://t.co/QGSJSEq4Ab pic.twitter.com/EIhZRVikK0— Aaron Bradley (@aaranged) March 15, 2021
"Information to Wisdom: Commonsense Knowledge Extraction and Compilation" from popular resources fortext extraction, e.g., #Wikipedia and scientific documents.(Razniewski et al, Tutorial at @WSDMSocial )paper: https://t.co/FiUedPOAzspage: https://t.co/YyuYX6xSCO pic.twitter.com/UvlM7bxqcW— WikiResearch (@WikiResearch) March 16, 2021
A Novel Paper Recommendation Method Empowered by Knowledge Graph: for Research Beginners https://t.co/K5TJEfEZcY pic.twitter.com/3JXpFUdds9— Aaron Bradley (@aaranged) March 17, 2021
"Open Graph Benchmark: Datasets for Machine Learning on Graphs" including the 'ogbl-wikikg2' datastet obtained from @Wikidata.(@weihua916 et al, 2021)paper: https://t.co/5GK0rM1wY0page: https://t.co/eTLSlyDgRN #KDD21 competition: https://t.co/vfLZ73Sqja@harvard_data pic.twitter.com/SGO5LyPjKW— WikiResearch (@WikiResearch) March 18, 2021
Business Persons' Guide to: What is RDF? In 10 minutes or less - YouTube
Part of my Quick Take Series for quick limited-technical explanations on topics, in this case What is RDF? or Resource Description Framework, a major part of...
"OntoEnricher: A Deep Learning Approach for Ontology Enrichment from Unstructured Text" - Bidirectional LSTMs trained on a large #DBpedia and #Wikipedia corpuses to enrich information security ontologies.(Mohan et al, 2021)https://t.co/5ZTYWrDSnu pic.twitter.com/z5XeiaTAf0— WikiResearch (@WikiResearch) February 15, 2021
"Digital Twins Definition Language is an open modeling language based on JSON-LD and RDF, by which developers can define the schema of the entities they expect to use in their graphs or topologies." This is an "open-source DTDL-based ontology .. for the real estate industry" https://t.co/icnwlMB5qy pic.twitter.com/at2kos9CDK— Aaron Bradley (@aaranged) February 16, 2021
Introducing Wiki Topic Grapher! 👾🐍🔥Leverage the power of Google #NLP to retrieve entity relationships from Wikipedia URLs or topics! + Get interactive graphs of connected entities+ Export results w/ ent. types+salience to CSV!▶️https://t.co/9M0zaMNIX8h/t @Streamlit 🧵 pic.twitter.com/ok9M3ypQgr— Charly Wargnier (@DataChaz) February 19, 2021
I4OC on Twitter: "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 🚀" / Twitter
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
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
[2102.10588] LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs
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...
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
Network mapping is a concrete method to include more voices in your reporting » Nieman Journalism Lab
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...
Benefits of Taxonomies for content information and knowledge management
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...
Business Persons' Guide to: What is a Knowledge Graph? In 10 minutes or less
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
Step-by-Step, No-Code Taxonomy Model ANYONE Can Learn
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...
