GraphNews

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

Stardog was predicated on a long-term bet that data management and knowledge management were on a collision course, set out by Google, and that Knowledge Graph was the intersection point. From 2023’s perspective, it looks like our long-term bet was correct.

The newest version of the LangChain library contains a Cypher Search module that allows you to query Neo4j using natural language. Very cool! | 15 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…

“Paper: Optimality of Message-Passing Architectures for Sparse Graphs. Work by @aseemrb. arXiv link: https://t.co/99Isy4Ul1n. I have been teaching a graduate course on graph neural networks this year. Close to the end of the course, many students noticed that all proposed…”

Orb is an open-source library developed by Memgraph you can use to visualize graphs by adding just a few lines to your frontend code. This blog post will show you all the cool features Orb offers and how to implement them in your project. Or don't and have slow and appalling graph visualizations - it's your choice. Seriously, use it... it's very easy and fun!

Learn how Graph Neural Networks and Knowledge Graphs can benefit your business with resources and Jupyter notebooks to help you get started today.

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.

One of the challenges for any Big Data solution is dealing with scale, and RDF stores are no exception: going for billions of RDF triples (the equivalent

Graph technology is well on its way from a fringe domain to going mainstream. We take a look at the state of the union in graph, featuring Neo4j's latest release and insights as well as data and opinions from Cloudera, DataStax, and IBM.

RDF is a graph data model you've probably either never heard of, or already dismissed. Why is that, could there be value in it, and how does it differ from the most popular graph data model out there?

DataStax Enterprise just released version 6.0, a major upgrade. We take an insider tour with Jonathan Lacefield, Senior Director of Product Management

It's official: AWS has a production-ready graph database. What features are included today, and what will be included in the near future, what use cases are targeted, and what does AWS Neptune's release mean for users and graph database vendors?

Having an entry path, as well as a strong community, is important for any solution, and graph databases are no different. Following latest developments in

GraphQL is a query language mostly used to streamline access to REST APIs. Now, a new breed of GraphQL implementations wants to build an abstraction layer for any database on top of GraphQL, and it seems to be catching up.

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.