GraphNews

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

A short article on reading-comprehension and knowledge-based dialogue systems with an overview of the SCAI workshop @ IJCAI 2019

The Centre for Digital Built Britain’s National Digital Twin programme has launched an open consultation seeking feedback on the proposed approach to the development of an Information Management Framework for the built environment. 

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

Talk about the Semantic Web and Linked Data, and the conversation will usually turn towards the concept of inferencing. Inferencing is the…

In 2019, I was asked to write the Foreword for the book “Graph Algorithms: Practical Examples in Apache Spark and Neo4j“, by Mark Needham and Amy E. Hodler. I wrote an extensive piece on the power of graph databases, linked data, graph algorithms, and various significant graph analytics applications. In their wisdom, the editors of the book decided that I wrote “too much”. So, they correctly shortened my contribution by about half in the final published version of my Foreword for the book.

For more complex products such as new pharmaceutical drugs, medical equipment or healthcare treatments, identifying hubs of influencers among physicians and other healthcare providers requires deep analysis of patient claims data to uncover the referral relationships.

How do you apply graph thinking to solve complex problems? With Practitioners Guide to #Graph #Data @DeniseKGosnell @MBroecheler show #datascientists how to think about data as graph, techniques for building scalable, real-time, multimodel architectures

model for capturing Cloud and HPC applications.

here's what's new in 20c: blogs.oracle.com/oraclespatial/… #oraclegraph #Analytics #DataScience https://t.co/hXlgPgxcxJ

The RDF Store Apache Rya is now a Top-Level Project, sounds like something to play with. It does SPARQL :) Anyone got some experience already?Fun fact: Its origins go back to NSAs Laboratory for Telecommunication Sciences https://t.co/KJfoEVK7tQ pic.twitter.com/mnRj5isYn4— Adrian Gschwend (@linkedktk) September 25, 2019

This story was originally published on InsideBigData .Knowledge graphs are one of the most important technologies of the 2020s. Gartner predicted that the applications of graph processing and graph databases will grow at 100% annually over the next few years.Over the last two decades, this technology was adopted mostly by engineers and ontologists, hence the majority of knowledge graph tools were designed for the users with advanced programming skills.In 1900, 40% of the population was involved in farming.Today it’s 1%. Coding is the modern day “farming” as only 0.5% of the world’s population knows how to code.NoCode brings equal opportunities to talent of all trades.Let’s just imagine, what impact this could have if the majority of world’s population was able to take advantage of cutting edge technologies to solve top of mind problems.Empowering top talent with NoCode approachEngineering and programming are important skills but only in the right context, and only for

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

In the spirit of H.R. 4174, EK can help make your data more organized, structured, and more accessible to technical and non-technical users.

Facilitating #journalism #automation via #knowledgegraphs. KG nodes corresponding to news articles, arrows show their connections. Generated using @sentilecto_NLU, allows navigating the spacial representation of a set of related texts #AI h/t @aaranged

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

There is a fair amount of confusion about who is who in the Semantic world, and what are the professions of Taxonomist, Data Librarian, Ontologist (no, not "oncologist" as LinkedIn would suggest) and a Semantic Architect. I will lay out my view on this topic.

Andrea Volpini describes a dance between machines and humans that means much of what we see is really a semi-automated approach to SEO. Absolutely brilliant insights. Brilliant enough to stop me talking too much, for once 🙂 A cycle of a machine and humans correcting, stimulating and teaching each other. Never forget that everything comes […]

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…

A semantic database is a set of interconnected resources, where each node (the starting and ending point of an arrow) is some kind of resource and each edge is a relationship.The graphs created by this are instrumental in creating context free applications and knowledge stores.

In this blog Ahren Lehnert looks at the source of the source. Information systems as ecosystems in which technology, people, and processes interact.

The #3dscenegraph automatic semantic labeling and computation code is now available at https://t.co/08lm1L35LX! To learn more about the project visit https://t.co/vT1sLlTosC pic.twitter.com/eh4xf9DG0v— Iro Armeni (@ir0armeni) February 19, 2020

There is a running joke in standards circles: God must love standards. He's made so many of them. If you spend enough time working with standards, ontologies, reference data or information modeling, you'll find yourself involved in the process of creating, modifying or defending specific standards.

The core business value and use cases for a well-designed effective enterprise taxonomy design have remained true over the years.

Connected Data London community cast. Gavin Mendel-Gleason: Quick overview of query language and how we got here