The Connected Data Podcast

Python offers excellent libraries for working with graphs: semantic technologies, graph queries, interactive visualizations, graph algorithms, probabilistic graph inference, as well as embedding and other integrations with deep learning. However, most of these approaches share little common ground, nor do many of them integrate effectively with popular data science tools (pandas, scikit-learn, spa...

Industry leaders from Accenture, Johnson & Johnson, and the Enterprise Knowledge Graph Foundation dive deep into the transformative potential of knowledge graphs, exploring how these semantic technologies are revolutionizing enterprise data management.  Featuring Mike Atkin, Laurent Alquier and Teresa Tung. The conversation reveals a critical shift from traditional data processing to a more nu...

Gary Marcus argues for a shift in research priorities, towards four cognitive prerequisites for building robust artificial intelligence: Hybrid architectures that combine large-scale learning with the representational and computational powers of symbol-manipulation Large-scale knowledge bases—likely leveraging innate frameworks—that incorporate symbolic knowledge along with other forms of knowledg...

Join Omar Khan and David Newman as they canvas the Enterprise Knowledge Graph, and how you can apply it using its cornerstones of: Foundational building blocks Information model expressivity Machine understandable representations Transcending the relational model How an EKG expands on a graph and a knowledge graph Provides an infrastructure for Machine Learning Contrasting an unlinked with linked...

Graph representation learning has recently become one of the hottest topics in machine learning. One particular instance, graph neural networks, is being used in a broad spectrum of applications ranging from 3D computer vision and graphics to high energy physics and drug design. Despite the promise and a series of success stories of graph deep learning methods, we have not witnessed so far anythin...

Connected Data is coming back to London in 2024, on December 11-13.  Join us for a tour de force in all things Knowledge Graph, Graph Analytics / Al / Data Science / Databases and Semantic Technology. Call for submissions and volunteers, program committee, chairs, and initial lineup have been announced. This online roundtable highlights the Connected Data landscape and how it's reflected in our Ca...

What does reasoning have to offer? How does it add so much value to data? Who is using it and why should I care? All questions that we’re delighted to answer. Access to data has exploded over the last decade, but it leaves us asking what to make of it all? Often lacking quality, reasoning is required to enrich data by adding context and insights, serving up knowledge, not just numbers. This expert...

An AI tsunami is on the rise, and the past few months have only amplified it. To survive it and thrive in tomorrow’s economy, organizations big and small must rethink the way they do business. To do this, a radical shift in the way they work with their data is needed. And no, we don’t mean Big Data. By now, most organizations have gotten their Big Data. And that is a problem. Not because we can’t...

Graph-based technologies became first-class citizens in various industries and many practical applications. Still, building performant and reliable machine learning pipelines over graph data, e.g., graph machine learning applications and products, remains a non-trivial task. This panel discussion brings together academic and industrial experts from fields where Graph ML yields significant gains an...

After the amazing breakthroughs of machine learning (deep learning or otherwise) in the past decade, the shortcomings of machine learning are also becoming increasingly clear: unexplainable results, data hunger and limited generalisability are all becoming bottlenecks. In this talk we will look at how the combination with symbolic AI (in the form of very large knowledge graphs) can give us a way f...

What do graphs have to do with novel hardware architectures for AI workloads? Graph processing is the key to unlocking new architectures, as much as new architectures can boost execution of graph-oriented workloads. As machine learning-powered applications are proliferating, the workloads that are created in order to serve their requirements are taking up an ever increasing piece of the compute pi...

While mathematicians have used graph theory since the 18th century to solve problems, the software patterns for graph data are new to most developers. To enable "mass adoption" of graph technology, we need to establish the right abstractions, access APIs, and data models. RDF triples, while of paramount importance in establishing RDF graph semantics, are a low-level abstraction, much like using as...

Taxonomies are the duct tape of connected data. They seem simple, flexible, and familiar. They are widely used. And they seem to work across many use cases and many domains.  But when looked at in more detail, taxonomies turn out to be crude tools for knowledge organization that are very difficult to create, to scale, to adapt, to align, and to build on. They don't work well for larger or more com...

What is Connected Data, and how is it interesting from a market point of view? Knowledge Graphs have reached peak Gartner hype. Graph data science and graph AI are the fastest growing areas in AI. Graph databases are the fastest growing category in enterprise software. Add to this the historical foundations of graph algorithms and analytics and semantic technology, which have been invigorated and...

JSON is the de facto data format for developers today because it’s easy to use, but it’s not without its issues. JSON-LD builds on top of JSON, and has also been called "the gateway drug" for Linked Data . Our panel of experts explores the many facets of JSON-LD and how it can facilitate enterprise data integration. Featuring Kurt Cagle, Freelance Technology Analyst, Brian Platz, co-founder and CE...

Graph Analytics has long demonstrated that it solves real-world problems including Fraud, Ranking, Recommendation, text summarization and other NLP tasks. More recently, Graph Machine Learning applied directly on graphs using graph algorithms and machine learning, has been demonstrating significant advantages in solving the same problems as graph analytics as well as problems that are impractical...

Are your personal data, documents, files and messages all over the place? Do you find yourself switching between applications, devices and files, unable to remember or find what you were looking for? Would it make you feel better to know that it's not entirely your fault, and maybe there is a way out? You know the stories about how the volume of data the world generates every day has gone through...

The AI industry is now facing its next big challenge. What are the necessary properties of representational structures that could allow vast amounts of data become meaningful in the human sense of the word? How can knowledge architectures be constructed in a way that allows for both the efficiency and effectiveness of models they support? In his Connected Data World 2021 keynote, Gadi Singer, VP &...

Most Major Companies are Exploring or Using Knowledge Graphs. Knowledge Graphs are at the top of the Garter AI Hype Cycle. But Knowledge Graphs are much more than hype! Knowledge graphs are a mature technology used in large scale deployments. Anyone heard of Google, Facebook, Alibaba, or Uber? Knowledge graphs address major weaknesses in traditional relational technology. These weaknesses are majo...

Join us as we have a sneak peek through the Connected Data World 2021 program, and discuss the Connected Data landscape. Our Program Committee members go through the 50+ sessions and 70+ speakers, and talk about: The Connected Data landscape Knowledge Graphs Graph Databases Graph Analytics Graph Data Science & Semantic Technology Topics, speakers and talks that piqued our interest Our own work...

What does graph have to do with machine learning? A lot, actually. And it goes both ways Machine learning can help bootstrap and populate knowledge graphs. The information contained in graphs can boost the efficiency of machine learning approaches. Machine learning, and its deep learning subdomain, make a great match for graphs. Machine learning on graphs is still a nascent technology, but one whi...

Personalized medicine. Predictive call centers. Digital twins for IoT. Predictive supply chain management, and domain-specific Q&A applications.  These are just a few AI-driven applications organizations across a broad range of industries are deploying. Graph databases and Knowledge Graphs are now viewed as a must-have by Enterprises serious about leveraging AI and predictive analytics within...

Connected Data encompasses data acquisition and data management requirements from a range of areas including the Semantic Web, Linked Data, Knowledge Management, Knowledge Representation and many others. Yet for the true value of many of these visions to be realised both within the public domain and within organisations requires the assembly of often huge datasets. Thus far this has proven problem...

"The most important contribution management needs to make in the 21st Century is to increase the productivity of knowledge work and the knowledge worker", said Peter F. Drucker in 1999, and time has proven him right. Even NASA is no exception, as it faces a number of challenges. NASA has hundreds of millions of documents, reports, project data, lessons learned, scientific research, medical analysi...

As the interest in, and hype around, Knowledge Graphs is growing, there is also a growing need for sharing experience and best practices around them. Let’s talk about definitions, best practices, hype, and reality. What is a Knowledge Graph? How can I use a Knowledge Graph & how do i start building one? This panel is an opportunity to hear from industry experts using these technologies & a...