Learn advanced Cypher queries for one approach to analyzing data inconsistencies directly in your flexible and forgiving Neo4j graph database.

Trace the safety information throughout the entire drug development lifecycle in a single intelligent system with Ontotext's solution.

Ontotext’s smart semantic search solution helps Pharma companies to seamlessly navigate data and information coming from various siloed data-sources.

This post shows how to build serverless GraphQL service by using Azure Functions and GraphQL for .NET.

With the release of Neo4j 4.0 comes an exciting new feature, called Neo4j Fabric. The operational principle of Neo4j Fabric is in essence quite simple – it provides a way to issue Cypher queries that target more than one Neo4j graph database at once.

As interest grows in new technologies, many enterprises are reconsidering their data warehouses. What makes graph databases different from traditional RDBMSs?

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Come to Amsterdam end of June and visit the SIGMOD Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA). #...

Financial institutions have massive volumes of highly interconnected and structured data, which they need to ingest in real time and to use for a wide range of queries and reports, from simple fund transactions to machine learning-driven fraud detection and risk analysis. To provide a standard format for representing and interchanging such data, the FIBO knowledge graph ontology was developed, to use with triple-store or RDF databases. However, while RDF databases are great for modeling and exchanging data, property graph databases offer a more efficient and intuitive object-oriented approa...

Siren 10.2.3 released! now supercharging #elasticsearch up to v6.8 (7 coming soon) and with @neo4j connectivity (blog post here https://t.co/eIU8t6AjUa) #augmentedanalytics #neo4j #devops #itops #knowledgegraph #investigative pic.twitter.com/xpE4Iq1xG8— Siren (@sirensearch) June 21, 2019

A newbie's guide to querying @Wikidata #opendata #datascience #GraphDB #KnowledgeGraph #tutorial #Neo4j #softwaredevelopment #linkeddata #connecteddata #data #tech @markhneedham @BarrasaDV

This article has two sections. The first describes the longstanding difficulties in integrating data for analytics and more recently, data science and AI.

More and more #opendata platforms are becoming linked #data. The ONS, @WikiData & Scottish Government all offer SPARQL endpoints to their #linkeddata. SPARQL acts as pattern-matching language @digidatajen @ODcamp #tutorial #knowledgegraph h/t @linkedktk

A standard query language for property graphs It's official. In June national standards bodies around the world belonging to ISO/IEC's Joint Technical Committee 1 (which is responsible for IT standards) started voting on the GQL project proposal.

We’re happy to announce the release of Stardog 7.0 Beta 2, complete with lots of changes designed specifically to help make the world’s best Knowledge Graph even more awesome.

Stardog, the leading provider of Enterprise Knowledge Graph technology, today announced the release of Stardog Sandbox, a free, hosted environment built to make knowledge graphs more accessible. Stardog Sandbox allows users to experiment with queries, mappings, and capabilities with no installation, setup, or configuration.

#DataManagement ‘useful fictions’ @StardogHQ @kendall @pymnts: Firms only exist to be the repository of a particular #data universe. Key that may differentiate Tier 1 bank from other banks: #data it has on hand, tied to specific operations, customers https://www.pymnts.com/data/2019/stardog-on-data-managements-useful-fictions/

Deploying Stardog Cluster on Kubernetes is a walk in the park. In this post we show you how easy it is and how it works.

GraphQL is going to eat most of the APIs, and SPARQL is for the hard stuff. They seamlessly interoperate in Stardog and we support both.— Stardog (@StardogHQ) November 26, 2018

/PRNewswire/ -- Stardog, the leading Enterprise Knowledge Graph platform, today announced the release of the first data model template compatible with the...

.@StardogHQ scores $9M Series B #funding to launch new product offerings, scale go-to-market initiatives. Stardog will expand its product offering to include #KnowledgeGraph as a Service, enabling hybrid #cloud environment #EmergingTech #AI #graphdatabase

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