market,” said Kendall Clark, CEO and Founder of Stardog. “We plan to expand our successful EU operations, strengthen our work in the public sector, and to develop tools and partnerships to broaden access to knowledge graphs.” Stardog’s Enterprise Knowledge Graph platform is used by industry leaders including Morgan Stanley, NASA, Schneider Electric and Bayer. Customers use Stardog for a range of solutions including operational resilienc

data lived in files. End of story. The files likely had different formats because each programmer was essentially maintaining their own information, but on the flip side the only person who had access to that data was the programmer, and that meant that your data was safe so long as your programmer was trustworthy.Networks had also been around since the 1960s, since computers were expensive and it was simply not efficient to have one programmer per computer. This meant that different people would have access to the same computer at the same time, usually by computers temporarily swapping out one person's data and swapping in someone else's so they had a slice of time on the CPU. That data was persisted when not in use in magnetic core memory i

TigerGraph is offering free Cloud and Enterprise Edition use for applications requiring massive data or high computation needs for Coronavirus research.

imec from Belgium. Semantic reasoning over streaming data Semantic reasoning with streaming data does not necessarily require specialised formalisms and tools. You can perform stream reasoning using Stardog with a relatively simple Python extension, which I’ll demonstrate below. Industry 4.0: Hello data Companies around the world are connecting their equipment, machinery and even people to the worldwide web. The Internet of Things (IoT for short) defines these connected things as smart devices, all of which are producing floods of data. This data has become a target for business intelligence and analytics to generate important business insights. However the raw data is often too opaque to process thus it has become a popular approach to annotate it with metadata and inte

LinkedSDGs: linking information resources & discovering connections to #SDGs 1) upload document 2) extract key concepts 3) find links to #SDGs & relevant stats #linkeddata & knowledge extraction resources @SustDev #opendata #data #tech #sustainability [LINK]https://www.linkedin.com/feed/update/urn:li:activity:6628584405906661376/ [LINK]https://media-exp1.licdn.com/dms/image/C5622AQELLgccoCKFCw/feedshare-shrink_800/0?e=1583366400&v=beta&t=pfry2nj7rsH7Ex5cOs36op-Wdn0Y1lyvsYVUhMzEj7k

Learn more about text-mined knowledge graphs and see how to build one with Grakn.

Photo by AndreyPopov on iStockWe all use the Google Knowledge Graph tens of times a day, but maybe not many of us are aware to be actually querying the Graph while making a simple search on Google.When you search for something, for example, “Goldman Sachs”, what you get is a list of snippets of web pages plus an Infobox next to the search results.The Knowledge Graph behind your Google search allows to enhance the search engine with specific and possibly useful features on the “entity” you are looking for (in this case Goldman Sachs), gathered from a variety of sources. So, allegedly, Google Knowledge Graph enhances the result of our search with semantics [4].Let’s now try to consider reasoning.Now, say we are studying Goldman Sachs for some reason and we wish to know whether there is some person x in Goldman Sachs board who is the CEO of some other company y that is in the Tech field?Or in other terms, in a ‘fancy’ logic conjunctive query fashion:∃ x y board(Goldm

WHAT THIS TALK IS ABOUT My personal take on the history of human label collection ter to you

I realized that I needed to know what the constituent parts of data models really are. Across the board, all platforms, all models etc. Is there anything similar to atoms and the (chemical) bonds that enables the formation of molecules?

What’s the difference between AnzoGraph and many other graph databases on the market? This infographic highlights the many nuances between them.

Data, AI, intelligent applications. They’re no longer separate topics, or even separate conferences. O’Reilly announced that it is merging its data and AI conferences, saying, “Data feeds AI; AI makes sense of data.” Further, both power applications. In 2016, Ben Lorica – then program chair of the O’Reilly Strata Data and AI Conferences – predicted that soon “some features of AI will be incorporated into every application that we touch, and we won’t be able to do anything without touching an application.”

This post is a series of 3 articles about the GraphTech ecosystem, as of 2019. This article is the first part and covers the graph database landscape.

Here at Neo4j, we have a motto: Graphs Are Everywhere. This blog series was inspired by all the times I encountered graphs and “graph problems” in my non-working life. Hopefully these posts help you see more graphs in the world.… Read more →

Boettiger, (2019). Ecological Metadata as Linked Data. Journal of Open Source Software, 4(34), 1276, https://doi.org/10.21105/joss.01276

The perfect dorm room assignment: Exploring the bipartite matching problem with #Neo4j #graphdatabase

I discovered this W3C workshop to be filled with a dedicated and diverse group of experts from industry and academia, who are passionate about extracting value from linked data

Learn how Knowledge Graphs are essential to unlocking digital transformation's business value.

For a domain with a strong focus on unambiguous identifiers and meaning, the Semantic Web research field itself has a surprisingly ill-defined sense of identity. Started at the end of the 1990s at the intersection of databases, logic, and Web, and influenced along the way by all major tech hypes such as Big Data and machine learning, our research community needs to look in the mirror to understand who we really are…

How graph databases can be used to explore and analyse the decentralised XRP ledger, focusing on exploring networks of fraudulent accounts and distribution patterns.

Early in 2018, industry analysts declared 2018 as the year of the graph. As we move into 2019, it is worth reflecting how the graph market has evolved.

20X more are likely untested, and over 200 deaths. The need to influence appropriate behavior and mitigation actions are extreme: The US has shot up from untouched to become the 6th most infected nation.

Explore everything that's happening in the Neo4j community for the week of 2 February 2019, including building a just-in-time data warehouse with Neo4j Streams.

Explore everything that's happening in the Neo4j community for the week of 9 March 2019, includign link prediction and what happens when you query a Neo4j cluster.

Explore everything that's happening in the Neo4j community for the week of 9 February 2019, including revealing malware relationships using Neo4j.

Discover what's new in the Neo4j community for the week of 18 July 2020, including Graph Embeddings and executives of Belgian companies.

Hi graph gang, In this week’s video, Jans, Niels, and Morgan show how to build a social knowledge graph based on data from collaboration tools that we use at Neo4j. Rik Van Bruggen queries the COVID-19 Contact Tracing Graph, Tomaz… Read more →

.@TigerGraph #graphDB updates its #Cloud offering with configuration for distributed graphs, replica instances for high availability, EFS for backup/restore. Updates available by end of 2019 on #AWS, #Azure to follow in Q1 2020

A year after emerging from stealth mode, graph database startup TigerGraph is expanding its offerings to include a cloud database service that supports AI

TigerGraph Launches PartnerGraph Program to Meet Growing Demand for Graph Analytics in AI and Machine Learning - New Program Enables Partners to Leverage Leading Scalable Graph Database and Analytics Platform