GraphNews

If information does not exist in electronic form by now in your organization, it is very likely already lost.

#knowledgegraphs need #DLTs to secure keys, DLTs need knowledge graphs to provide context & provenance. Ultimately, knowledge graphs will end up being the integration point for a number of #technologies lumped under #AI #data #EmergingTech @kurt_cagle

“You must unlearn what you have learned” (Master Yoda) The “conceptual” level

.@shivamshaz #datascientist who rules #DataScience for Good competitions on #Kaggle wants to apply expertise in #ML to solve micro-finance problems for underbanked population in developing countries using network science, graph theory, unstructured data

The emerging landscape for distributed knowledge, #ontology, #semanticweb, knowledge base, graph based technologies and standards. Current trends related to graph based #technology #knowledgegeraph #analytics #graphDB #AI #datascience #longread @nfigay

Much (if not all) of the discussion about temporal issues in the last 30+ years have been based on the assumption of the necessity of SQL tables. The narrative for how to build “well-formed” SQL data models is the well-known “Normalization” procedure. Data modelers with my hair color will remember the poster, which was a give-away with the Database Programming and Design Magazine (Miller Freeman) in 1989. The title of the poster is “5 Rules of Data Normalization”. Here is a miniature image of it

Meetup organised by Connected Data London, 29th January 2019, London. Don't miss the first meet up of the year!

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.

This post presents the ecosystem of graph visualization. Read on to find the full list of graph visualization software and graph visualization libraries.

This post is a series of 3 articles about the GraphTech ecosystem. This article covers the graph analytics landscape: frameworks, libraries, query languages

The Great(er) Bear - using @Wikidata to generate better artwork. Wikipedia holds structured #data about people and things. It uses SPARQL to query that data. @edent writes something to query Wikidata to generate a more accurate artwork #dataviz #graphDB

Lee — the computer scientist best known as the inventor of the World Wide Web — himself who dreamt of a place full of readable data, neatly linked. Years later, we are working towards that goal, thanks to a vocabulary called Schema. This article tells you a bit more about how we got here.

an ontology representating the knowledge of the aviation sector

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 →

designed in at the start. Big companies have thousands of them. In the 1990s, co

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tier_architectureHowever, knowledge graphs propose a paradigm shift to this design blurring the barrier between logic and data. By bringing some of the knowledge of the domain into a graph through rules a knowledge graph captures more than just the data in the system. As a result, rules can make the queries and requests much simpler to write and manage which in turns allows applications to be more flexible, less error prone and faster.This article will introduce a simple example to showcase the impact of rules on query design. The example will be ill

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with [...]

Relational databases despite their name are not very good at expressing relationships in data because they impose a storage structure which does not favour connections.Graph databases despite their name look nothing like a graph but offer greater flexibility for expressing relationships between data points…

JanusGraph is a popular open source property graph database that serves a variety of transactional and analytical use cases. It was originally designed to run on top of a number of different distributed storage engines including Apache Cassandra and Apache HBase. This talk will discuss the new JanusGraph FoundationDB storage adapter which adds distributed ACID support to JanusGraph. Topics will include an intro to JanusGraph and FoundationDB layer development followed by a deep dive into how the property graph model and read/write access patterns have been mapped on to FoundationDB. We will...

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

Recipes for Knowledge Graphs that WorkLearn why and how to build knowledge graphs that help enterprises use data to innovate, create value and increase revenue. This practical manual is full of recipes and knowledge on the subject.Learn more about the variety of applications based on knowledge graphs.Learn how to build working knowledge graphs and which technologies to use.See how knowledge graphs can benefit different parts of your organization.Get ready for the next generation of enterprise data management tools.Click here to get the book as free PDF or Kindle version. Sign up for the Webinar on Wednesday,

Although we use the term “knowledge” broadly used in the AI and graph database community, it has many different meanings based on the…

thanks for making them available Ruben! twitter.com/RubenVerborgh/…

A piece that attempts to clarify what metadata is, and why it can be so helpful to content. Answers a frequently asked question that our integration team receives.

The most depicted humans in #paintings #SPARQL query on #Wikidata looks an #artwork itself https://t.co/MgWi4bCDPo (ht @sanseveria ) pic.twitter.com/qMjZtNkCFM— Maarten Dammers (@mdammers) January 18, 2020

Multi-model is a useful #technology for #enterprise #knowledgegraphs, say @arthurakeen @arangodb Benefits: streamlining multi-source #data to EKGs, increasing usability of EKG data for business, enabling greater scale, reducing EKG ecosystem footprint