Olaf Hartig on Twitter
Cool! RDF* support in Neo4j's RDF plug in. https://t.co/f7t2Iyo8rY— Olaf Hartig (@olafhartig) September 24, 2020
TigerGraph on Twitter
“How do you select a graph database? Learn how in @itworldca. Read here: https://t.co/Z529PWdeW6 | #GraphDatabase #GraphDB #GraphAnalytics #DataScience #Developer #Analytics #BigData”
Synaptica LLC on Twitter
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 on Twitter
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
Frank Dellaert on Twitter
“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...
stephen mallette on Twitter
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
Alan Morrison on Twitter
"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
WikiResearch on Twitter
😉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
Learning SPARQL on Twitter
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
Klaus Illmayer on Twitter
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
Kelvin Lawrence on Twitter
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
James Le on Twitter
https://t.co/kSTvjW2oBv Such a thoughtful article on #knowledgegraphs! @TDataScience pic.twitter.com/BphubNoYwv— James Le (@le_james94) October 1, 2020
Anthony J. Algmin on Twitter
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
Marco Neumann on Twitter
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
TigerGraph Unveils Free TigerGraph Enterprise Edition, Helping Companies Use Graph as the Foundation of Many Modern Data, Analytics and AI Capabilities
𝖱uud Steltenp∞l ❤️🤔🔗📊,💾🌳,🚲🌍,👨👩👧👧 on Twitter
Adrian Suciu on Twitter
Happy to announce that my @OReillyMedia book Semantic Modeling for Data is now published https://t.co/4yngwDPMrO and available in electronic and print format https://t.co/VsFc8zf2KY. Get a free sample chapter at https://t.co/DivwADNUGo #datascience #datamodeling #knowledgegraphs pic.twitter.com/9j58IF1lcZ— Panos Alexopoulos (@PAlexop) September 9, 2020
Edge properties, part 1: Reification
W3C LBD CG meets … – Selected Topics on Linked Data, Semantic Web and Graph Technologies in the Built Environment
Aaron Bradley on Twitter
More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings - Andreea Iana, @heikopaulheim https://t.co/ZlN50kgXrk— Aaron Bradley (@aaranged) September 2, 2020
WikiResearch on Twitter
Our #Yahoo! Knowledge Graph version of #Wikipedia entity embedding is now publicly available. This will be the version we use to trigger the related entity search for knowledge panels in Yahoo! Search, try it if you need general entity embedding in any task. @wikiworkshop https://t.co/eB9H6ai2zI— Chien-Chun Ni (@saibalmars) September 2, 2020
WikiResearch on Twitter
"VisualSem: a high-quality knowledge graph for vision & language", based on #Babelnet and #Wikipedia(Alberts et al, 2020)https://t.co/mWNh7QxXpZ@claravania#NLProc #ComputerVision pic.twitter.com/s8bGn6MAdV— WikiResearch (@WikiResearch) September 3, 2020
10 Graph Algorithms Visually Explained
A quick introduction to 10 basic graph algorithms with examples and visualisationsContinue reading on Towards Data Science »
Day 228 of #NLP365 - Learn NLP With Me – Knowledge Graph On Finance (Balance Sheets)
sheets/ Best, Ryan submitted by /u/RyanAI100
Python Weekly - Issue 462
Knowledge Graphs vs. Property Graphs – Part 1
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades. Over the last few years, a number of new graph databases
Subgraphing without subgraph()
Subgraphing is a common use case when working with graphs. We often find ourselves wanting to take some small portion of a graph and then operate only upon it. Gremlin provides subgraph() step, which helps to make this operation relatively easy by exposing a way to produce an edge-induced subgraph that is detached from the parent graph.
Notes on graph theory — Centrality measures
suited tool to present data where connections and links are important for us to understand it. Like molecules structure that presents a collection of basic atoms which are linked to other, forming complex structure where each atom’s connection in this collection means something’s in terms of the usage or the characteris
Ontotext GraphDB Named Champion in Bloor's Graph Database Market Research
Technology research and analyst house Bloor Research places Ontotext as champion among RDF graph providers in its latest update on the graph database market
Do Graph Databases Scale? - DZone Big Data
Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it. All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks. But the nature of graph data poses challenges when it comes to *buzzword alert* scalability. So why is this, and are graph databases capable of scaling? Let’s see... In the following, we will define what we mean by scaling, take a closer look at two challenges potentially hindering scaling with graph databases, and discuss solutions currently available. What Is the “Scalability of Graph Databases”? Let’s quickly define what we mean here by scaling, as it is not “just” putting more data on one machine or throwing it on various ones. What you want when working with large or growing datasets is also an acceptabl