consider how you can effectively use JSON-LD as the foundation of your data architecture
In today's data-driven world, it is crucial to establish a clear boundary between your public and private data. Whilst banks, medical institutions, and spy… | 35 comments on LinkedIn
Embrace Complexity — Conclusion Building Your Organisation's Knowledge Graph
A powerful idea has been slowly building for many years now, originally known as the Semantic Web, and then later as Linked Data. This idea has finally... 27 comments on LinkedIn
Signal AI opens External Intelligence Graph for enterprise use
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
Know, Know Where, KnowWhereGraph: A densely connected, cross‐domain knowledge graph and geo‐enrichment service stack for applications in environmental intelligence
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...
DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation? - DataScienceCentral.com
Can Machine Learning Do Symbolic Manipulation? I spent some time over the holidays engaged in a fascinating online conversation. The gist of it was a variation of an argument that has been going on in the realm of artificial intelligence from the time of Minsky and Seymour Papert: Whether it is possible for neural networks to… Read More »DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation?
How and why the best companies are adopting Graph Visual Analytics, Graph AI, and Graph Neural Networks. By Leo Meyerovich and Ben Lorica. [A version of this post originally appeared on the Graphistry blog.] In this post, we highlight the current state of Graph Intelligence, a new technology category around new tools and techniques forContinue reading "What is Graph Intelligence?"
Understand the ROI of an Enterprise Knowledge Graph Platform | Stardog
Enterprise Knowledge Graph Platform ROI: Providing readers with a framework to evaluate the potential financial impact of deploying Stardog on their own organizations.
London-based Memgraph raises over €8 million in seed funding to provide Streaming Graph Algorithms to the masses
Memgraph, the streaming graph application platform, today announced Memgraph 2.0, the public launch of its source-available platform, which makes it easy
Giancarlo Guizzardi on LinkedIn: #wikidata #linkeddata #conceptualmodeling | 23 comments
Types x Individuals, Instantiation x Specialization. These are among the most fundamental distinctions in Conceptual Modeling. Still, they seem to elude... 23 comments on LinkedIn
GitHub - pykeen/pykeen: 🤖 A Python library for learning and evaluating knowledge graph embeddings
🤖 A Python library for learning and evaluating knowledge graph embeddings - GitHub - pykeen/pykeen: 🤖 A Python library for learning and evaluating knowledge graph embeddings
