GraphNews

This document provides an overview of two modeling strategies/patterns used for building knowledge graphs and triplestores of core biological ‘knowledge’ (e.g. relations between genes, chemi…

With AgensGraph, an Education Service Company 34-years in the Making Builds a Big Data Graph Database for Education

Last week I was part of team from Optum ventured out to Stanford University in Palo Alto CA for a three-day conference sponsored by the…

Read this blog to learn how graphs revolutionized how hackers attack, and how SpecterOps uses Neo4j to access a company's cybersecurity measures.

Data languishes in silos and most enterprises sponsor initiatives to free that data so every part of the organization can mine it for insights. The challenge is that these “silo-breaking” projects are complex, expensive, require organizational change, have a long time-to-value, and do not scale.

MP voting records are public record in the UK, detailed #data is available about how each MP has voted in each bill @PublicWhip collect data & make it accessible @joshua_e_k explores how to use a #GraphDatabase to look at MP’s voting record similarity

A blog about my work and fun with Neo4j, the world's leading graph database platform.

So can any SemWeb gurus explain to me what the difference is between SHACL and ShEx? If SHACL is an official W3C recommendation, why use ShEx? Why is effort being split on this? Not a criticism at all, but a genuine question as I'm new to all this and only familiar with SHACL.— Bryan J. Brown (@bryjbrown) November 26, 2018

At Build 2019 this week, Microsoft introduced support for a Spark API that will enable Cosmos DB to support hybrid transaction and analytic processing workloads (HTAP).

Check out all the latest Microsoft Graph news from Build 2019

Blockchains are often complemented by databases, but in some scenarios, a database solution like Azure Cosmos DB can supply the core characteristics of the blockchain itself. Whether you’re looking...

By Patrick Chase and Satish Dharmaraj

2 together with its impact on human health. One aspect of this is organizing existing and emerging information about viral and host cell molecular biology, disease epidemiology, phenotypic progression, and effect of drugs and other treatments in individuals.

start your development. The following diagrams illustrate the collection of data around a given user, such as device identifiers, cookies, browse

Presentation Summary

The Codex is a project that aims to achieve deep integration between text and structured data by building a graph network of people, places, events and concepts embedded in texts. #NLP services are incorporated to drive #semantic search @codexeditor

Check out my other blogs here!Microsoft gives a wonderful tutorial about Knowledge Graph in KDD 2018. If you are a machine learning engineer or an NLP engineer, I highly recommend reading this tutorial. It talks about what is knowledge graph (KG), the KG construction challenges for a large scale, and the…

PubMed is an essential resource for the medical domain, but useful concepts

It seems like every crime show includes a few scenes where we see that the detective has built a wall of pictures, newspaper clippings, index cards and other interesting documents, linking all these things together through a network of string and push pins. This linked data allows them to step back and see how the facts relate and helps provide the bigger picture of what happened and how to solve it. Seeing the bigger picture allows detectives to use inductive and deductive reasoning to pursue leads,identify gaps in their knowledge and continue the investigation in ways they may not have seen before.The crime wall is a physical representation of knowledge or context. The crime wall helps investigators see the relationships and understand the true meaning of the facts surrounding a case. Understanding context can lead to accelerated insights and increase the productivity of the detectives. In the digital world, we can represent knowledge through similar techniques. We call this di

quality knowledge and expertise. Modern organizations expose their knowledge with conversational interfaces such as bots and expert systems so customers, partners, and employees will have immediate access to the knowledge that drives success. We, data scientists and engineers, are responsible to make that happens. We need to answer a simple question: How do you represent business knowledge so it is easy and simple to consume? There are many approaches and possible strategies for exposing knowledge. in this article I want to dig into the good old Frequently Asked Questions system and discuss how to implement it with the latest AI technologies.In the prehistoric era, websites used to have this one FAQ page with a long and tedious list of useless questions. Only real optimists would ever search this list to find a possible remedy for an issue they face. Those years are gone. Today modern web sites have an integrated bot that user

This article takes an in-depth look at building enterprise performance into a graph database and also explores graph databases.

If we fail to predict the future they call us a failure, and if we do it too well they call us a sorcerer.

If we fail to predict the future they call us a failure, and if we do it too well they call us a sorcerer.

Leveraging the Neo4j Community Graph For Social Discovery

Built-in Jupyter notebooks for Azure Cosmos DB are now publicly available. Developers, data scientists, engineers and analysts can use the familiar Jupyter notebooks experience to interactively run...

Looking for a way to connect your BI solution with ArangoDB? Qlik, Tableau, Power BI or Grafana

Mixing graph and relational data is now even more accessible. What does this mean for analytics, and for graph databases specifically?

When extracting information from documents using NLP, attempting to separate important terms from noise can be tricky. Calculating a TF-IDF (Term Frequency-Inverse Document Frequency) score allows you to identify the important terms in a document.

#CaLiGraph, a #knowledgegraph which comes with 5.6M instances and a rich ontology with 750k classes and 200k class restrictions compiled from the @DBpedia ontology and @Wikipedia Categories & Listpages announced by @heikopaulheim @nheist @dwsunima http://caligraph.org