Recently RedisGraph published a blog [1], comparing their performance to that of TigerGraph’s, following the tests [2] in TigerGraph’s benchmark report [3], which requires solid performance on 3-hop, 6-hop, and even 10-hop queries. Multi-hop queries on large data sets are the future of graph analytics....

literally as well as figuratively (through investments, supplying

DQN)

After building and selling Heyzap back in 2016 I had free reign to dig into the question of “what should I really be working on?” and how to create something that would have a lasting impact on society. In part of this journey I’ve had the privilege to invest

Until now the only way to run Graph Algorithms on Neo4j has been to learn Cypher. The Graph Algorithms Playground changes all that.

With our semantic chatbot, semantize your data without even having to understand what data semantization is!

This is what the resultant 100 retail outlet isochrone map looks like, built using Spatia and @rapidsai #cuGraph SSSP using @OrdnanceSurvey Open Roads as road graph + drive times estimated from @transportgovuk road stats #opendata cc @puntofisso pic.twitter.com/rGhDinkaVX— John Murray (@MurrayData) May 28, 2019

.@jQAssistant is a #QA tool which allows definition & validation of project specific rules on a structural level. Built upon #Neo4j #graphdatabase, can be plugged into build process. Now w/ #PlantUML class diagrams #dataviz #softwareengineering

Visualize Kafka Streams with Neo4j by taking any data, turning it into a graph, leveraging graph processing, and piping the results back to Apache Kafka, adding visualizations to your event streaming applications.

In part 1 of this article series, I provided a quick primer on graph data structure, acknowledged that there are several graph based algorithms with the notabl…

.@DDIAlliance releases XKOS v1.2 (Extended Knowledge Organization System) specification →facilitates sharing & management of statistical classifications.(For building #Semantic Taxonomies to tag Datasets & #DataScience outputs)https://t.co/HZD1nl5Nu0#BigData #Ontologies #RDF pic.twitter.com/DDYbHqCvm7— Kirk Borne (@KirkDBorne) June 21, 2019

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We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades. Over the last few years, a number of new graph databases

based models have shown their unique advantages in knowledge graph (KG)

It was hard to top last year’s demonstration of the new Microsoft 365 experienced powered by the Microsoft Graph. The focus this year shifted to how the graph could break down application and device silos to make everyday applications more user-centric.

While the term “Knowledge Graph” is relatively new (Google 2012) the concept of “representing knowledge as a set of relations between entities - forming a “graph” has been around for much longer. 2019 marks, for example, the 20th anniversary of the publication of arguably the first open standard for

Discover what's new in the 3.5 release of the Neo4j graph database, including a Go language driver, full-text search and indexing, TLS encryption and more.

based Gruff Drives Infinite Data Integration, Holistic Insights and Complex Reasoning

“Q: which product taxonomies are used in the Shopping / Ad industries? Google's Product Taxonomy is a de facto standard but it lacks freshness, coverage and/or finesse in some areas. I'm also looking at product taxonomies from Amazon, Ebay, Walmart, Target, Groupon. What else?”

Graphs are common #data structures to represent #connecteddata. To use graphs with #deeplearning, we use graph embeddings, a low dimension representations which helps generalize input data. Node2Vec aims to preserve network neighborhoods #datascience #AI

https://douroucouli.wordpress.com/2019/05/10/obographviz-0-2-2-released-now-with-visualization-of-equivalence-cliques/

I have submitted mine. Find it at https://t.co/tdWjN8NhdX"The RDF* and SPARQL* Approach to Annotate Statements in RDF and to Reconcile RDF and Property Graphs"— Olaf Hartig (@olafhartig) January 11, 2019

Keynote talk given at the 41st European Conference on Information Retrieval (ECIR 2019)

Obfuscation traditionally gives analysts a bit of a hard time. Graph databases can help look past the smoke and mirrors. Learn more on the G DATA TechBlog!

Technology research and analyst house Bloor Research places Ontotext as champion among RDF graph providers in its latest update on the graph database market

Norconex crawlers can now be used to harvest relationships from websites and filesystems and feed them to Neo4j.

Updates on #knowledgegraphs affect services built on top of them. But not all changes are the same: some updates drastically change the result of operations based on knowledge graph content; others do not lead to any variation #research #award #iswc_conf

Querying the first RDF Data Cube based on an existing PC-Axis/PX data cube published by @swissstatistics. Thanks to @bergi_bergos for the great converter, will be made public later pic.twitter.com/4SJ3jpKg7e— Adrian Gschwend (@linkedktk) July 10, 2019