Thoughts on semantic layers, data transformations, data apps and the future of the data stack.

AWS developed the Deep Graph Knowledge Embedding Library (DGL-KE), a knowledge graph embedding library built on the Deep Graph Library (DGL). DGL is a scalable, high performance Python library for deep learning in graphs. This library is used by the advanced machine learning systems developed with Trumid to build a credit trading platform.

In a distributed world where data is federated in a Mesh internally and you value comes from how you collaborate externally the most…

Happy to announce that we just published our #linkeddata extension for #VSCode. Visualize, transform, validate and query your #rdf directly on your files...

At IKEA we are building a knowledge graph to improve the overall experience of our customers in the physical and digital space. I like to…

You thought you knew all normal forms? Well, think again: There is also “graph normal form,” based on mathematics going back 25 years.

Nature Biotechnology - A knowledge graph platform integrates proteomics with other omics data and biomedical databases.

Signal AI unveiled its new tool, a data structure that constantly tracks the major and minor events for companies that course through the news sphere each day.

Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics. — Timothy G. Mattson, Senior Principal Engineer, Intel Corp Expertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. Massive Graph Analytics will be beneficial to students, researchers, and practitioners in academia, national

Data Commons brings together sustainability-related data from over 100 sources — on everything from temperature to water availability.

Knowledge graphs help understand relationships between the objects, events, situations, and concepts in your data so you can readily identify important patterns and make better decisions. This book provides tools and techniques for efficiently labeling data, modeling a knowledge graph, and using it to derive useful insights. /b In Knowledge Graphs Applied/i you will learn how to: Model knowledge graphs with an iterative top-down approach based in business needs/li Create a knowledge graph starting from ontologies, taxonomies, and structured data/li Use machine learning algorithms to hone and complete your graphs/li Build knowledge graphs from unstructured text data sources/li Reason on the knowledge graph and apply machine learning algorithms/li /ul Move beyond analyzing data and start making decisions based on useful, contextual knowledge. The cutting-edge knowledge graphs (KG) approach puts that power in your hands. In Knowledge Graphs Applied/i, you’ll discover the theory of knowledge graphs and learn how to build services that can demonstrate intelligent behavior. You’ll learn to create KGs from first principles and go hands-on to develop advisor applications for real-world domains like healthcare and finance.

Apollo's new platform, Supergraph, is a collection of tools that can work together to generate reports, dashboards and general answers.

Co-authors: Joshua Shinavier and Shirshanka Das

Last week I had the pleasure and privilege of attending the 4th annual Knowledge Graph Conference (KGC) at Cornell Tech in NYC. In addition to contributing my own talk on “Boutique Knowledge Graphs” and participating in a panel on “Content Knowledge Graphs,” I attended two days of inspiring and insi

Logseq is pitching a privacy-focused open-source platform that helps people manage and collaborate around their knowledge bases.

ISI's Center on Knowledge Graphs research group combines artificial intelligence, the semantic web, and database integration techniques to solve complex information integration problems

Creates the first relational knowledge graph system; adds Bob Muglia to its board

Topic Modelling: A primer and A mental model. After Philip Vollet’s post on Top2Vec, many of you pinged. So here you go. 👉 Criteria A: Discover topics... 15 comments on LinkedIn

Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beg...

Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologie...

The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.

Knowledge graphs, the technology powering Google, Facebook and Apple, is now unlocking value across the financial sector. Knowledge graphs are transforming critical capabilities and enterprises are increasingly looking to the technology to enhance their tax strategy, perform compliance and improve customer service.

A new generation of note-taking tools helps us quickly organize thoughts as knowledge graphs

Automating language is not magic; it can take years of careful work to piece together what people mean in the context of corporate help desks, HR programs and the like. Such is the work of startup PeopleReign.

While the NLP field has been growing at an exponential rate for the last two years — thanks to the development of transfer based models — their applications have been limited in scope for the job…

Nature Machine Intelligence - The number of graph neural network papers in this journal has grown as the field matures. We take a closer look at some of the scientific applications.

Data on Native communities are not at the levels needed for accuracy in A.I.-driven tools. A group is trying to solve that problem.