Resources

Accelerating Data Science with Knowledge Graphs

Semantic knowledge graph platforms–like our Anzo–help organizations overcome scalability challenges by accelerating core data science processes like data preparation and feature engineering.

Ontology Management with a Scaleable Knowledge Graph

In this video Stephen Kahmann from
Cambridge Semantics’ partner iNovex answers your questions about the use of a knowledge graph to enable ontology management.

Managed Vocabularies, Ontologies, and an Enterprise Scale Knowledge Graph: A Primer

This blog provides a primer on the concepts of managed vocabularies and ontologies and how they relate to and can be implemented in a knowledge graph. This is an excerpt from the ebook The Rise of the Knowledge Graph.

The State of the Knowledge Graph

For our knowledge quest enjoyment the authors of the recent O’Reilly ebook The Rise of the Knowledge Graph are doing a live webinar exploring the universe of knowledge graphs and their role in data-intensive use cases like customer 360, fraud detection, drug and clinical data analysis, and other advanced analytics.

Knowledge Graph Conference

If you’re attending the upcoming virtual Knowledge Graph conference (May 3-6, 2021), be sure to join us in two sessions: a panel discussion with knowledge graph leaders from Merck KGaA, Deloitte, and Bosch on May 6 at noon, and a presentation on building an enterprise scale knowledge graph with Ben Szekely on May 5 at 3:00 pm.

The Fate of Feature Engineering: No Longer Necessary, or Much Easier?

In this contributed article, editorial consultant Jelani Harper believes that features are the definitive data traits enabling machine learning models to accurately issue predictions and prescriptions.

Knowledge Graph: Who Cares? And What Can One Do With It?

In a previous post that described knowledge graphs and their benefits, we discussed how knowledge graphs help organizations make more of their enterprise data easy to find, understand, and use. This post argues for knowledge graph technology and discusses common application areas of knowledge graphs.

Machine Understandable Context and Why It Matters

This post continues the larger discussion of knowledge graphs, their roles, uses and significance. We discuss context, it’s importance, and how to establish it using formal ontology to create machine understandable information. It is useful to read posts, such as Knowledge Graphs: A Love Affair and The Rise of The Knowledge Graph, to understand this article…in context!

Knowledge Graphs: Origins, Inhibitors and Breakthroughs

This post discusses some history of knowledge graph technology, what went wrong, and key advances that have allowed knowledge graphs to reenter the enterprise “data arena.” We also highlight some important outcomes of the technology.

AI, ML and Advanced Analytics Strategies: Look Before You Leap

The phrase, “garbage in, garbage out” comes to mind. In other words, logically, if one’s premise is flawed, so also are one’s conclusions. Nevertheless, we can’t seem to tame our fascination with “shiny objects.” This article discusses a “prerequisite” foundation on which to build successful AI, ML and advanced analytics capabilities.

The Rise of the Knowledge Graph

Recently writer and technologist, Dean Allemang joined forces with Cambridge Semantics’ founders, Sean Martin and Ben Szekely, to write a definitive guide all things knowledge graph related. The Rise of the Knowledge Graph offers anyone building a knowledge graph or just interested in the topic a solid introduction to this emerging data management architecture.

Knowledge Graphs: A Love Affair

This article describes the concept of the knowledge graph, how knowledge graphs support data interoperability and some of their benefits. The reader will gain an understanding of what a knowledge graph is, it’s role in data interoperability, and be more prepared to understand their role in data integration and access.