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.

Graph data linked with geographic data will provide new means of data discovery, developers were told at a recent conference hosted by Esri.

In the last couple of months, I’ve been noticing a gradual shift in the kind of articles that we receive at Data Science Central. We still get a fair amount of data science content, but increasingly (and admittedly with a bit of encouragement) we’re seeing more articles centered around graphs and semantics. I don’t believe… Read More »DSC Weekly Digest 22 February 2022: Graphology

It is nearly certain that, if you have ever played a 3D video game, watched a CGI-effects-laden movie, or seen increasingly hyperrealistic imagery, you have encountered a scene graph without realizing it. Scene graphs are pervasive in everything from media to medicine, from augmented reality to industrial digital twins, and they are increasingly playing an… Read More »Scene Graphs and Semantics

The Semantic Web has garnered a reputation for complexity among both Javascript and Python developers, primarily because, well, it’s not JSON, and JSON has become the data language of the web. Why learn some obscure language when JSON is perfectly capable of describing everything, right? Well, sort of. The problem that JSON faces, is actually… Read More »Why JSON Users Should Learn Turtle

Just because you’re calling your model a “knowledge graph” doesn’t make it so. True knowledge graphs possess three distinct characteristics.

🧪 From an internal lab project to a project for the future of Dataveyes 🚀 We are very proud to introduce Modality, our analysis and visualisation tool... 36 comments on LinkedIn

Tim Berners-Lee has an interesting habit of coming up with ideas that seem hard to explain at the outset, remain all hard to understand even as they become more implemented and refined, can go for years with only a few die-hard fans becoming convinced that what he is doing is the best thing since sliced… Read More »How Solid Pods May End Up Becoming the Building Blocks of the Metaverse

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