My new paper on ontologies in Data and SW engineering https://t.co/jqQAEMZbEl @nimonika— Christian Dirschl (@HaintzD) January 3, 2019

Albert Einstein once said “Imagination is more important than knowledge. Knowledge is limited; imagination encircles the world.” While…

up Lisp development style that I have used for decades using symbolic AI and knowledge representation."

Luis Bosquez, Program Manager for Azure Cosmos DB shares an overview of the Graph/Gremlin API, and best practices app developers can use when building apps using the graph data model and Apache Tinkerpop Gremlin language. http://www.azurecosmosdb.com Graph/Gremlin API - Documentation: https://docs.microsoft.com/en-us/azure/cosmos-db/graph-introduction About Azure Cosmos DB: Azure Cosmos DB is a fully-managed NoSQL database service offering unlimited and elastic scalability of throughput and storage, and guaranteed speed and performance anywhere in the world.

The unprecedented in-depth analysis enabled by graph analytics can produce risk warnings faster and better than was possible ten years ago.

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Creating Graphs in Python using Networkx #dataviz #tutorial #datascience #data #tech

explorerThe keyword has and hasLabel does all the m

Knowledge graphs have emerged as a compelling abstraction for organizing world's structured knowledge over the internet, capturing relationships among key entities of interest to enterprises, and a way to integrate information extracted from multiple data sources. Knowledge graphs have also started to play a central role in machine learning and natural language processing as a method to incorporate world knowledge, as a target knowledge representation for extracted knowledge, and for explaining what is being learned. This class is a graduate level research seminar featuring prominent researchers and industry practitioners working on different aspects of knowledge graphs. It will showcase how latest research in AI, database systems and HCI is coming together in integrated intelligent systems centered around knowledge graphs.The seminar will be offered over Zoom as per the planned schedule.The seminar is open to public. Remote participants may join the seminar through Zoom. To be

In this post I explain common issues with query performance in Neo4j and how you may solve them using the Query Log Analyser tool.

does it support reverse direction edges?— Dan Brickley (@danbri) March 18, 2019

“We are also releasing the first published embeddings of the full @Wikidata graph of 50M Wikipedia concepts, which serves as structured data for use in the AI research community. The embeddings can help other researchers perform machine learning tasks on Wikidata concepts.” https://t.co/1nqwaJvD8d— Dario Taraborelli (@ReaderMeter) April 3, 2019

Because Graph Analytics is the future

Graph databases are receiving a lot of hype these days because of the promise of fast and flexible queries that aren’t possible within either traditional RDBMs or NoSQL stores built on simple/singular access patterns. There are some practical tips and tricks that ensure that your graph database project is going to live up to the hype. In this talk, we will walk through the data modeling tips and tricks that are being used to help graph users achieve success. We’ll also highlight how to avoid the largest graph problem that can plague any graph database project, the dreaded supernode. This wi...

sheets/ Best, Ryan submitted by /u/RyanAI100

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

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

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?

Do you want to learn more about #knowledgegraphs and data-centric architecture? @AlanMorrison put together a list of resources #datacentric #dataarchitecture #semantics #LinkedData #GraphDatabase

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

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How can investigation into money distribution and tracking funds cross the borders of blockchains?

based search systems do not reflect the semantics of individual input words of search queries. For example, a query for the word “house” would not return records for the words “building” or “real estate”. How can such relationships be represented in a technical system? One approach is to include synonyms. Search engines like Elasticsearch provide methods to integrate synonym lists. However, a list of synonyms itself is required for configuration.

We just released PSPS: an open source tool allowing Linked Data sites based on GitHub repositories. Like GitHub pages but self-hosted and with SPARQL and RDF. Check it out: https://t.co/tBu6xvA2Qa - As you might have guessed: that's the software powering https://t.co/olmahLZFvb— FactsMission AG (@FactsMission) January 10, 2019

Stardog 6.2 just shipped with scalable virtual graph caching, better Kubernetes integration, support for Amazon Redshift, and many new optimizations . Read on for the details.

Feeling SHACL'd to your desk at home these days? Then try out SHACL Play!, a "free online SHACL validator for RDF data" from Thomas Francart bit.ly/2QfpbQ5