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Tag Archives: Machine Learning

Graphs in the cloud. The Year of the Graph Newsletter: April 2019

The first sign of convergence in the graph space is here. Graph databases continue to grow, expand, and make their way to the cloud, a number of open source frameworks for working with graphs has been released, and a slew of new interesting use cases. One of the takeaways of the recent W3C Workshop on […]

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Knowledge graphs, AI, and interoperability. The Year of the Graph Newsletter: February 2019

Knowledge graphs are spreading everywhere: from Airbnb and eBay to Alexa, and from using JSON-LD on the web for better SEO to leveraging taxonomy to define AI. Combining knowledge graphs and machine learning, benchmarking graph databases, and W3C initiative for interoperability shaping up. January 2019 has been a lively month in the graph landscape. A […]

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The Year of the Graph Newsletter: November 2018

October 2018 was the busiest month in the busiest year in graph history, hence the longest Year of the Graph newsletter to date. Neo4j lands a massive funding round, Tinkerpop is moving forward, the most important knowledge graph research event with key industry presence, W3C organizing a Workshop on Web Standardization for Graph Data, and […]

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The Year of the Graph Newsletter: September 2018

Knowledge graphs in Gartner’s hype cycle, machine learning extensions and visual tools for graph databases, Ethereum analytics with RDF, Using Gremlin with R, SPARQL, and Spring, graph database research wins best paper award in VLDB, and benchmarking AWS Neptune. Not bad for a typical summer vacation month such as August. This edition of the Year […]

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The Year of the Graph Newsletter: July 2018

What is a graph database? Do you really need one, and if yes, how do you choose? That’s what it all comes down to. This month’s edition of the Year of the Graph newsletter is special. Apart from the usual hubbub, which is somewhat slower this time of year, this month the Year of the […]

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