GraphNews

TerminusDB is an open source model driven graph database for knowledge graph representation designed specifically for the web-age.

Use new Geospatial and GeoSPARQL functionality in AnzoGraph DB to develop large scale location intelligence and geospatial applications along-side rich data-analytics using SPARQL* and RDF*.

AnzoGraph is a fast, horizontally scalable, OLAP graph database that brings a wealth of analytics capabilities to large graphs

A technologist and DZone Leader sat down with an expert in big data and NoSQL databases to discuss his creation, AnzoGraph, and how it applies to big data.

Learn all about the positive vote to include Cypher queries and graph algorithms on DataFrame-based property graphs as part of Apache Spark 3.0.

Free @DataStax Academy course Getting Started w @apachetinkerpop & Gremlin. To be familiar with Gremlin traversal syntax & techniques, developers need to understand how the language works @ABridgwater @DeniseKGosnell #tutorial #softwaredevelopment

In one of the biggest AI events in the world, over 3,000 experts from research and industry showcased and discussed their latest work. Advances in machine learning are happening across the board, and integrating knowledge-based systems with graph-based deep learning promises breakthroughs.

We are super excited to share the latest upgrades to ArangoDB which are now available with ArangoDB 3.5. With the fast-growing team, we could build many new and long-awaited features in the open-source edition and Enterprise Edition. Get ArangoDB 3.5 on our download page and see all changes in the Changelog. Need to know more […]

Phew, it’s been quite a ride, but today the whole team is super excited to announce a $10 million Series A funding for ArangoDB, our native multi-model database. We feel honored and frankly a bit proud that Bow Capital is leading this investment round and shows their trust in our product, team, and amazing community. […]

Graph Convolutional Networks (GCNs) generalize the idea of deep convolutional

The drug discovery process is hugely data-intensive, making it an ideal application for artificial intelligence (AI) and machine learning. But…

Data Lakes will evolve into knowledge graphs. This article is aimed at explaining the meaning of Knowledge graphs based on semantic web and why it will eventually secure its rightful place in organizing enterprise knowledge.

Semantic standards have been with us since the birth of the web, when it became clear to inventors of the web like Tim Berners-Lee and many others that meaning could be systematically captured, organized, and exploited to do valuable tasks. Since then, the idea of applying formal semantics to enterprise data has come and faded […]

At the heart of things, an enterprise knowledge graph supports decision and process augmentation based on linked data. These models of knowledge domains are created by subject matter experts with the help of machine learning algorithms. They live as virtual data layers on top of existing databases or data sets to link both structured and unstructured data at scale and across disparate data silos.

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In just six months since its release, Google’s pretrained model BERT (Bidirectional Encoder Representations from Transformers) has become…

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Working for over two decades in Business Intelligence and Analytics nothing has bothered me more than being tied to a relational model.

Role of Augmented Analytics in Future !! Insight generation is changing (and That’s a Good Thing) Have you ever been in situation when you need quick insigh…

Augmented analytics tools, NLP search and graph analytics are the top trends for 2019 and beyond. Experts say augmented analytics and NLP are transforming how enterprises consume data and derive in...

/PRNewswire/ -- Aurea, the company behind some of the world's greatest customer and employee experiences, including Jive's collaboration solution, today...

Apply web scraping bots , computational linguistics, and natural language processing algorithms to build knowledge graphsContinue reading on Towards Data Science »

#Amazon #NLP service for #healthcare, Comprehend Medical, can now link information to medical #ontology. It's designed to understand relationships between things like dosage & conditions, improve medical outcomes & care #knowledgegraph #innovation

AWS Data Migration Service (DMS) now supports migrating graph data from relational sources to Amazon Neptune. The AWS Database Migration Service (AWS DMS) enables you to migrate data from one data source to another. Using relational databases as source and Neptune as destination allows customers to copy their connected data into Neptune for graph queries.   Customers using both relational and graph databases today manually load their data into Neptune. DMS minimizes the manual effort to carry out the workflow. Using DMS, you can configure a destination endpoint to existing Neptune databases. DMS will carry out a full copy of data from relational databases to Neptune. The DMS workflow allows you to target either a RDF model or a property graph model by specifying the appropriate mapping file for each data model. DMS version 3.3.2 supports Amazon Neptune as the destination endpoint. You can configure DMS using the AWS Management Console, AWS SDK or CLI. You will be ch

The Azure Cosmos DB team is excited to announce version 3.0 of the .NET SDK, now in public preview and open sourced on GitHub!

Cosmos DB's entry level pricing has come way down, opening up the service to a much broader audience. And Azure Machine Learning gets new versatility, including usability from virtually all Python-language programming environments.

We're excited to announce the public release of Akutan, a distributed knowledge graph store, under the Apache 2.0 open source license. Akutan is the result of four person-years of exploration and engineering effort, so there's a lot to unpack here! This post will discuss what Akutan is, how it's implemented, and why we've chosen to release it as open source.

.@NebulaGraph is a new #opensource #GraphDB, claiming to be capable of hosting super large scale graphs w/ dozens of billions of nodes, trillions of edges, milliseconds of latency. Inspired by #Facebook project Dragon, featured on page #1 on @HackerNews