Augmented analytics, continuous intelligence and explainable artificial intelligence (AI) are among the top trends in data and analytics technology that have significant disruptive potential over the next three to five years, according to Gartner, Inc.

Gartner Names PoolParty as Visionary in Metadata Management Magic Quadrant. Our modern approach combining machine learning and knowledge graphs makes the difference!

As a follow up of word embedding post, we will discuss the models on learning contextualized word vectors, as well as the new trend in large unsupervised pre-trained language models which have achieved amazing SOTA results on a variety of language tasks.

Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org - Accenture/AmpliGraph

A comprehensive collection of recent papers on graph deep learning - DeepGraphLearning/LiteratureDL4Graph

Material for VKG2019 tutorial at ESWC2019. Contribute to oeg-upm/vkg-tutorial-eswc2019 development by creating an account on GitHub.

Cypher for Gremlin adds Cypher support to any Gremlin graph database. - opencypher/cypher-for-gremlin

SHACL2SPARQL is a prototype Java implementation of the algorithm described in #iswc19 best #research paper award winner Validating SHACL constraints over a SPARQL endpoint (Corman, FLorenzano, Reutter & Savkovic). #opensource #github #softwaredevelopment

Making RDF easy enough for average developers. Contribute to w3c/EasierRDF development by creating an account on GitHub.

Albany, US, 2019-Jan-23 — /EPR Network/ —Market Research Hub (MRH) has actively included a new research study titled “Global Graph Database Market” Size

Weaviate #Knowledgegraphs. Difference between #database & #knowledgegraph @SeMI_tech on @GCPcloud stores KGs in a space, it's API-based, does auto-classification. Uses #opendata to train graph #machinelearning #cloud #opensource @markmirch @bobvanluijt [LINK]https://www.youtube.com/watch?v=SOUtWj2szOM&feature=emb_title [LINK]https://i.imgur.com/wG2P3yi.png

How Google may build and improve upon the Google knowledge Graph using knowledge graph reconciliation to remove ambiguity and add new entities

Want to build from these basics. an introduction:

Google is testing the ability for people to customize their own Knowledge Graph cards that are displayed in search results.The post Google Lets People Customize Their Profile Cards For Search Results via @MattGSouthern appeared first on Search Engine Journal.

related on Google. I was very excited when I discovered this latest enhancement as I believe it will benefit patients the most. In reality, most patients don’t know what type

#Google #ProductSearch & Learning about New Product Lines. Patent analysis by @bill_slawski Google working hard to improve its product #knowledgegraph to compete with #Amazon by offering free / unpaid (non-ads) product results h/t @cyberandy #SEO #search [LINK]http://www.seobythesea.com/2020/01/new-product-lines/ [LINK]http://www.seobythesea.com/wp-content/poduct-search.jpg

on for experimenting with natural language models. The tech giant describes it as a demonstration of how natural language understanding (NLU) can be used with pretrained, generic AI models, as well as a means to dispel intimidation around using machine learning.

Google has rolled out the new feature ‘People Card’ that enables business people or anyone who wants themselves to be easily identified. This feature is first rolled out in India, because of being the second largest internet market in the world. The feature is currently supported only in the English language for now, though other languages would be added in the future. The people card feature uses Google’s Knowledge Graph to display information provided by the user. The People card feature can be obtained by using your Google Account and by providing the mobile phone number.

Entity Extractions from Text on Web pages, instead of being limited to knowledge bases, may be used by Google to build knowledge graphs

DataStax is the company behind the massively scalable, highly available, cloud-native NoSQL data platform built on Apache Cassandra™.

Learn more about the Closeness Centrality graph database algorithm, which measures how a central a node is within its cluster.

based simulations in which an agent

Property graphs provide a lot of flexibility in data modeling; but how do you know when to use which feature?

When building graph data models we frequently have to deal with a degree of polymorphism for our entities just like the real world. For instance – I’m a person, but I’m also a parent, a spouse, a sibling, a child, a… Implicit categorisation Sometimes the entity categories are entirely defined by relationships to other entities. […]

The graph database market is very exciting, as the long list of vendors continues to grow. You may not know that there are huge differences in the origin story of the dozens of graph databases on the market today. It’s this origin story that greatly impacts the superpowers and weaknesses of the various offerings.

Graph databases and document databases make up a subcategory of non-relational databases or NoSQL. NoSQL databases were created to get a handle on large amounts of messy Big Data, moving very quickly. Managers use the non-relational toolkit to gain business insights and detect patterns in information on the fly, as Big Data streams into the system. Many companies, especially those with a large web presence like Google, Facebook, and Twitter, consider NoSQL databases a must-have.

Graph databases are shaped the way we think, so why can't people get their heads around them?

How can companies step themselves into the world of graph databases? Neo4j thinks it has an answer. It has been offering a Startup Program for startups with 19 employees or fewer; more than 650 startups with fewer than 20 employees took advantage of having free access to Neo4j Enterprise clusters.