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.
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!
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.