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Designing a Linked Data developer experience | Ruben Verborgh
Designing a Linked Data developer experience | Ruben Verborgh
Making decentralized Web app development fun ◆ While the Semantic Web community was fighting its own internal battles, we failed to gain traction with the people who build apps that are actually used: front-end developers. Ironically, Semantic Web enthusiasts have failed to focus on the Web; whereas our technologies are delivering results in specialized back-end systems, the promised intelligent end-user apps are not being created…
·ruben.verborgh.org·
Designing a Linked Data developer experience | Ruben Verborgh
Detecting Cryptocurrency Fraud with Neo4j
Detecting Cryptocurrency Fraud with Neo4j
Criminals are constantly finding new and more sophisticated ways to commit fraud. Every technological development presents new opportunities for illicit activities, and few more so than the evolution of digital currencies.
·neo4j.com·
Detecting Cryptocurrency Fraud with Neo4j
Developers | Zazuko
Developers | Zazuko
In case you have little to no experience with RDF you might want to read the RDF Primer first, which gives a good basic introduction to the concepts of RDF.
·zazuko.com·
Developers | Zazuko
Diego Moussallem retweeted: New from Google Research! REALM: realm.page.link/paper We pretrain an LM that sparsely attends over all of Wikipedia as extra context. We backprop through a latent retrieval step on 13M docs. Yields new SOTA results for open do
Diego Moussallem retweeted: New from Google Research! REALM: realm.page.link/paper We pretrain an LM that sparsely attends over all of Wikipedia as extra context. We backprop through a latent retrieval step on 13M docs. Yields new SOTA results for open do
New from Google Research! REALM: https://t.co/kS2oTyxAAjWe pretrain an LM that sparsely attends over all of Wikipedia as extra context. We backprop through a latent retrieval step on 13M docs. Yields new SOTA results for open domain QA, breaking 40 on NaturalQuestions-Open! pic.twitter.com/DYDFX69Td8— Kelvin Guu (@kelvin_guu) February 11, 2020
·twitter.com·
Diego Moussallem retweeted: New from Google Research! REALM: realm.page.link/paper We pretrain an LM that sparsely attends over all of Wikipedia as extra context. We backprop through a latent retrieval step on 13M docs. Yields new SOTA results for open do
Diego Moussallem on Twitter
Diego Moussallem on Twitter
Our physical embedding model for #knowledgegraphs achieve quasi-linear scalability. Check out the video by @_CaglarDemir at https://t.co/9n8tkZoWXg #MachineLearning #OpenScience @knowgraphs— Axel Ngonga (@NgongaAxel) February 14, 2020
·twitter.com·
Diego Moussallem on Twitter
Diffbot's Approach to Knowledge Graph | LinkedIn
Diffbot's Approach to Knowledge Graph | LinkedIn
Google introduced to the general public the term Knowledge Graph (“Things not Strings”) when they added the information boxes that you see to the right-hand side of many searches. However, the benefits of storing information indexed around the entity and its properties and relationships are well-kno
·linkedin.com·
Diffbot's Approach to Knowledge Graph | LinkedIn
Digital Tools for Looking at Texts
Digital Tools for Looking at Texts
constructed reports, that is easy, but as they become long, then things get really difficult. Thankfully there are a whole bunch of tools that are now becoming available that allow for the extraction of insights.The tools are possible owing to the advancements in natural language processing — the use of computational techniques and models to analyse natural language — language as it is used around us — in the documents, in voice, in chats. The explosion of content generated, and especially Wikipedia — has made various advancements possible. Thanks to Wikipedia, which contains topics arranged in a structured manner, and thanks to the effort put into translation by Go
·medium.com·
Digital Tools for Looking at Texts
Do Graph Databases Scale? - DZone Big Data
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
·dzone.com·
Do Graph Databases Scale? - DZone Big Data
Dr Nicolas Figay posted on LinkedIn
Dr Nicolas Figay posted on LinkedIn
Dr Nicolas FigayDigital Enterprises Organisation and Collaboration around Manufacturing and Product Data2w · EditedEmerging Landscape of #graphs related technologies: required move from da facto standards to #ISO open standard?
·linkedin.com·
Dr Nicolas Figay posted on LinkedIn
Alan Morrison on Twitter
Alan Morrison on Twitter
Do you want to learn more about #knowledgegraphs and data-centric architecture? @AlanMorrison put together a list of resources #datacentric #dataarchitecture #semantics #LinkedData #GraphDatabase
·twitter.com·
Alan Morrison on Twitter
eBay’s New Approach to Managing a Vast Service Architecture
eBay’s New Approach to Managing a Vast Service Architecture
For #eBay, the #application/infrastructure #knowledgegraph is a heterogeneous property graph that improves architectural visibility, operational efficiency, #developer productivity. How it was developed, benefits, use cases #data #analytics #devops #AI [LINK]https://tech.ebayinc.com/research/ebays-new-approach-to-managing-a-vast-service-architecture/[/LINK] [IMAGE]https://tech.ebayinc.com/assets/Uploads/Editor/_resampled/ResizedImageWzE2MDAsNjcwXQ/image66.png[/IMAGE]
·tech.ebayinc.com·
eBay’s New Approach to Managing a Vast Service Architecture
Emil Eifrem on Twitter: "So yeah. Today Gartner named Graphs on their top 10 trends for data in 2019, stating that "graph DBMSs will grow at 100 percent annually through 2022." 💪💪💪… https://t.co/HHgtrC6yuu"
Emil Eifrem on Twitter: "So yeah. Today Gartner named Graphs on their top 10 trends for data in 2019, stating that "graph DBMSs will grow at 100 percent annually through 2022." 💪💪💪… https://t.co/HHgtrC6yuu"
So yeah. Today Gartner named Graphs on their top 10 trends for data in 2019, stating that "graph DBMSs will grow at 100 percent annually through 2022." 💪💪💪 pic.twitter.com/LsCg38eyON— Emil Eifrem (@emileifrem) February 18, 2019
·twitter.com·
Emil Eifrem on Twitter: "So yeah. Today Gartner named Graphs on their top 10 trends for data in 2019, stating that "graph DBMSs will grow at 100 percent annually through 2022." 💪💪💪… https://t.co/HHgtrC6yuu"
Emotion Recognition Using Graph Convolutional Networks
Emotion Recognition Using Graph Convolutional Networks
This article summarizes recent #research on Emotion Recognition Using Graph #NeuralNetworks. DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation #AI #deeplearning #knowledgegraph #datascience #innovation #tech #data https://towardsdatascience.com/emotion-recognition-using-graph-convolutional-networks-9f22f04b244e?source=rss----7f60cf5620c9---4
·towardsdatascience.com·
Emotion Recognition Using Graph Convolutional Networks