We interviewed Varun Shenoy, a senior at Stanford who created a tool named GraphGPT.

By Dr. Verónica Espinoza, 2023 / Twitter @Verukita1

📢 the RDF-star Working Group at W3C has published 16 First Public Working Drafts, which represent the first milestone in the update of the #RDF and #SPARQL…

Are you considering deploying a language model like ChatGPT in your organisation, but have concerns about risk and governance? 🤔   🔗 Harnessing the Power of… | 32 comments on LinkedIn

When I was a grad student studying Artificial Intelligence back in the 80’s, Expert Systems were all the rage. Our “Holy Grail” was to…

I was at the Knowledge Graph Conference this week, and as usual, it was a fun and insightful time. I have enough topics for at least four…

At the Knowledge Graph Conference last week, a number of data managers and researchers from IKEA presented their work on a recommendation…

In the 4th part of this series of posts we look at a different way of data modeling for APIs, one that is based on Linked Data standards. First, some

I've been meaning to revisit some of the things i've been writing about and getting feedback on lately - APIs, the JSON vs. XML "non" debate and Linked

Defining semantics is a matter of semantics, not less so in the Big Data space.

Data integration may not sound as deliciously intriguing as AI or machine learning tidbits sprinkled on vanilla apps. Still, it is the bread and butter of many, the enabler of all things cool using data, and a premium use case for concepts underpinning AI.

Creating winning analytics solutions means combining and making the most of different approaches and techniques. Taking a look at how Google does this for YouTube can provide inspiration and set a framework for analytics solutions.

In a rapidly shifting job market, being able to formalize skills is a requirement for job seekers and employers going forward. Open Badges is a data specification that may be able to help do that.

Notepads, graphs, data lakes, collaboration, and data manifestos. Data.world has an interesting blend of philosophy and technology going on -- and it all converges around one thing: Facilitating data-driven analysis by making it a team sport.

Did you ever need data on a topic you wanted to research, and had a hard time finding it? Wish you could just Google it? Well, now you can do that.

Yes, Facebook is a data-driven monopoly. But the only real way to break it up is by getting hold of its data and functionality, one piece at a time. It will take a combination of tech, data, and social engineering to get there. And graphs -- personal knowledge graphs.

Being disrupted by Big Tech is one of the greatest concerns for any business. Good news: There may be a path to accelerate digital transformation and out-compete Big Tech, by leveraging domain knowledge.

You don't have to be a fortune teller to identify AI as the key trend for the 2020s. But there is nuance regarding AI hardware and software that deserves to be highlighted.

Data.world wants to eliminate data silos to answer business questions. Their bet to do this is to provide data catalogs powered by knowledge graphs and semantics. The choice of technology seems to hit the mark, but intangibles matter, too.

What exactly are knowledge graphs, and what's with all the hype about them? Learning to tell apart hype from reality, defining different types of graphs, and picking the right tools and database for your use case is essential if you want to be like the Airbnbs, Amazons, Googles, and LinkedIns of the world.

Siren promises the benefits of knowledge graphs, without the hassle of reinventing your existing data management and infrastructure

Knowledge graphs are among the most important technologies for the 2020s. Here is how they are evolving, with vendors and standard bodies listening, and platforms becoming fluent in many query languages

State of the art in analytics and AI can help address some of the most pressing issues in scientific research. Here is how top scientists are using them to facilitate coronavirus research.

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.

Data lakes can be a great asset, but they need an array of elements to work properly. We take a look at how it works for Montefiore Health System and discuss the role of semantics and graph databases in the data lake architecture.

Knowledge graphs are probably the best technology we have for data integration. But what about application integration? Knowledge graphs can help there, too, argues EnterpriseWeb.

Explainable AI in real life could mean Einstein not just answering your questions, but also providing justification. Advancing the state of the art in natural language processing is done on the intersection of graphs and machine learning.

A tour de force on progress in AI, by some of the world's leading experts and venture capitalists.

Gary Marcus, a prominent figure in AI, is on a mission to instill a breath of fresh air to a discipline he sees as in danger of stagnating. Knowledge graphs, the 20-year old hype, may have something to offer there.

In the new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. Here's a shortlist of technologies and processes that can support this transition, and what they are about.