Apache Tinkerpop rocks DataStax support for Gremlin - Open Source Insider
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
Apple, Alibaba, Amazon, and the gang promote state of the art in AI and Knowledge Discovery with Graphs | ZDNet
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.
ArangoDB Boosts Multi-Model Database Scalability Across Distributed Environments with Release of ArangoDB 3.5
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 […]
ArangoDB receives Series A Funding led by Bow Capital - ArangoDB
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. […]
Are Knowledge Graphs the future of Data Lakes ? - Knoldus Blogs
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.
Are Semantics the Undead of Enterprise Tech? They Keep Coming Back to Life — Early Adopter
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 […]
Artificial Intelligence and Enterprise Knowledge Graphs: Better Together - DATAVERSITY
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.
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, graph are trending
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...
Aurea PeopleGraph Unlocks the Value of Relationship Intelligence Starting with Jive Enterprise Communities
/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 »
AWS adds ontology linking to Comprehend Medical natural language processing service
#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 now supports copying graph data from relational sources to Amazon Neptune
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
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.
Being on Hacker News Front Page Brought Us Much More than Just 300+ Stars on GitHub within 24 hours
.@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
Benefitting from SPARQL 1.1 Federated Queries with Amazon Neptune
Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. Neptune supports the W3C’s graph model RDF, and its query language SPARQL. SPARQL 1.1 Federated Query specifies an extension to SPARQL for running queries distributed over different SPARQL endpoints.