GraphNews

#Amazon Neptune #graphDB now supports @apachetinkerpop 3.4.1. @gfxman shows examples of new features in the Gremlin query/traversal language #softwaredevelopment #analytics #database #data #tech #tutorial #opensource #AWS [LINK]https://muawia.com/amazon-neptune-now-supports-tinkerpop-3-4-features/[/LINK] [IMAGE]https://s.put.re/NzhUFENd.png[/IMAGE]

Amazon Neptune now supports the Apache TinkerPop 3.4.1 release. In this post, you will find examples of new features in the Gremlin query and traversal language such as text predicates, changes to valueMap, nested repeat steps, named repeat steps, non-numerical comparisons, and changes to the order step. It is worth pointing out that TinkerPop 3.4 […]

#graphDB #Amazon Neptune now supports full-text search integration with Elasticsearch clusters. Using Elasticsearch users can run full-text search query types such as match query, intervals query, query strings using extensions to Gremlin & SPARQL #AWS

The latest Amazon Neptune release brings together a host of capabilities that enhance developer productivity with graphs. This post summarizes the key features we have rolled out and pointers for more details. Getting started This new engine release will not be automatically applied to your existing cluster. You can choose to upgrade an existing cluster […]

#Amazon Neptune #graphDB now offers a workbench, an in-console experience to query your graph. It lets users query Neptune w #Jupyter #notebooks using Gremlin or SPARQL #datascience #cloud #knowledgegreaph #AI #database AWSreinvent2019

enterprise context, the problem arises of managing information sources that do not use the same technology, do not have the same data representation, or that have not been designed according to the same approach. Thus, in general, gathering information is a hard task, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure are unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. Over the years, several data integration solutions have been proposed:

The autonomous driving (AD) industry is exploring the use of knowledge graphs

An interactive map of all the world's disputed areas from #wikidata. Built in 10 minutes by @planemad, using SPARQL #dataviz #knowledgegraph #opendata #graphDB #data #tech #EmergingTech #datascience Map #visualization on left-side menu h/t @LearningSPARQL [LINK]https://t.co/a4Yngspwds[/LINK] [IMAGE]https://pbs.twimg.com/tweet_video_thumb/EKBb_NGW4AIYhEH.jpg[/IMAGE]

Heritage institutions are places in which works of art, historical records, and other objects of cultural or scientific interest are sheltered and made accessible to the public. The equivalent of that in the digital world, is already taking shape, through digitization and sharing of digital-born or

An Introduction to Graph Theory by @mpvenables Before diving in, we need to understand #data structure & networks in #machinelearning. Networks are useful for #apps, from driving directions to social networks #datascience #tutorial #analytics #AI [LINK]https://towardsdatascience.com/an-introduction-to-graph-theory-24b41746fabe[/LINK] [IMAGE]https://miro.medium.com/max/480/1*rnZ3FbSvWMVvcRP78fXeCg.png[/IMAGE]

Neural Networks aimed at effectively handling graph data.Photo by Alina Grubnyak on UnsplashGraph structured data is common across various domains, examples such as molecules, { social, citation, road } networks, are just a few of the vast array of data which can be represented with a graphs. With the advancements of machine learning we witness the potential for applying intelligent algorithms on the data which is available. Graph Neural Network is the branch of Machine Learning which concerns on building neural networks for graph data in the most effective manner.Notwithstanding the progress made with ML in the computer vision domain with convolutional networks, Graph Neural Networks (GNNs) face a more challenging problem, they deal with the awkward nature of graphs. Differently from images and text, graphs do not have a well defined structure. A graph’s node might have no connections or many, of which could be directed or undirected. Graphs in a dataset may have a variable

This first general textbook An introduction to ontology engineering has as main aim to provide the reader with a comprehensive introductory overview of...

Etsy has an uncontrolled inventory; unlike many marketplaces, we offer an unlimited array of one-of-a-kind items, rather than a defined set of uniform goods. Etsy sellers are free to list any polic…

This guide will help you understand JSON-LD and structured data markup. Go beyond the online generators and prepare your web pages for the future of search!

Learn how the HPE Ezmeral software portfolio can empower your business with intelligence, automation, security, and the ability to modernize your applications—fueling data-driven digital transformation, from edge to cloud.

Analyzing US Lobbying Data in #Neo4J #graphdatabase

28% of 20 million websites reviewed by @woorank are already using Structured Data #connecteddata #knowledgegraph #SEO #semantics #SchemaOrg h/t @cyberandy For a hands-on, in depth tutorial: [LINK]https://www.slideshare.net/ConnectedDataLondon/from-knowledge-graphs-to-aipowered-seo-using-taxonomies-schemas-and-knowledge-graphs-to-improve-search-engine-rankings-and-web

The new language model our teams built is the largest and most powerful one ever created – a milestone with the promise to transform how technology understands and assists us. https://t.co/YvLM0HAr8u— Satya Nadella (@satyanadella) February 12, 2020

https://t.co/7T0EOs6yG7 - Made this tiny tool to discover related subreddits.The graph is created based on jaccard similarity between two subreddits. Jaccard similarity is constructed from set of shared users.Source code https://t.co/J9r1jl1JjR pic.twitter.com/4hcg7mI4sg— Andrei Kashcha (@anvaka) January 10, 2019

.@CamSemantics announces Anzo #GraphDB v.2: RDF*, Custom SDK, Free Edition. "Imagine being able to do labeled properties, just like you do in Neo4j & other property graphs, but also have capability of RDF to help w ontologies & inferencing" #data #tech

powered applications with minimum friction.Broad compatibility with existing and future software developm

Michael K. Bergman announces his new book, A Knowledge Representation Practionary: Guidance from Charles Sanders Peirce. The book applies this guidance to the question of how to best represent human knowledge to computers. The book's practical guidelines should be of interest to any enterprise KM ma

Read this blog to learn about the exciting news that Neo4j Aura is now available on Google Cloud Platform.

grade features and scale. We appreciate your candid stories and collaboration, and we’ve used this to create a better solution. As such, we’re excited to announce Neo4j for Graph Data Science™, the first data science environment built to harness the predictive power of relationships for enterprise deployments. Neo4j for Graph Data Science is an ecosystem of tools that includes: With Neo4j for Graph Data Science, data scientists are empowered to confide

Learn more about Neo4j Labs, an innovative incubator with a focus on integrations, tools and libraries that extend the Neo4j Graph Platform.

Today we’re happy to announce the GA release of Stardog 7, including new low-level storage engine based on RocksDB. Read on for the glorious details.