Save the Date:
Wednesday, April 22, 2020
Knowledge Graphs are certainly nothing new, but they have only been in use in industrial environments for a few years now. Accordingly, one speaks of ‘Enterprise Knowledge Graphs‘ (EKGs). It refers to a wealth of approaches and technologies, all of which are aimed at getting a better grip on the chaos in enterprise data management. A central problem here is the siloing of data and the resulting additional costs and inefficiencies that arise along the entire data life cycle.
There are countless articles, slide decks and videos on the Internet about knowledge graphs. The topic is examined from different perspectives, e.g., from the point of view of artificial intelligence or in the context of extended possibilities for data analysis or information retrieval. Various standards, methods and technologies are suggested to the reader, and the moment of overstrain and disorientation, typical for the arrival of new technologies, quickly arises—so one wonders: “isn’t there a step-by-step recipe out there for how to create a knowledge graph like there is for classic dishes like Wiener schnitzel?”
That’s why Andreas Blumauer and Helmut Nagy wrote The Knowledge Graph Cookbook. The book is intended to help bring together and network different aspects of knowledge graphs. It will serve as a ‘cookbook’ for upcoming projects that want to integrate knowledge graphs as a central element. And above all, it should provide a quick overview of why every data and content professional should or must deal with the topic in greater detail.
In this webinar, Andreas and Helmut will walk you through the cookbook. You will learn:
- Why knowledge graphs are important and why you should care about them (‘Hunger is the best sauce’)
- How to get started with your knowledge graph initiative (‘Prepping the Kitchen’)
- Which personas are typically involved (‘Too many cooks?’)
- How to make knowledge graphs work (‘The proof is in the pudding!’) based on a set of basic ingredients and recipes
- How a system architecture could look like (‘A great chef is first a great technician’), and finally
- How the future of knowledge graphs could look like (‘Read the tea leaves’) with an outlook on how the post-corona society will deal with knowledge technologies and AI