An extremely powerful and efficient use for Knowledge Graphs is to unite well-understood domains of knowledge alongside novel and specific business/scientific questions. Using small ontologies that embed large reference taxonomies, we are able to go from tactical scientific questions (bottom-up) to common taxonomies and reference datasets (middle-out) approach to model scientific questions, aligning with enterprise master data strategies on the way up. If building blocks follow FAIR (Findable, Accessible, Interoperable, Reusable) data principles, reusing these processes becomes more and more efficient over time as the middle layer grows. Examples in the translational medicine space will be highlighted.
Francois Scharffe leads the Q&A between Vassil Momtchev from Ontotext and John Sowa from Kyndi.
Law firms are starting to build knowledge graphs to power next-generation marketing and business development applications that efficiently integrate data and help deliver the most important intelligence insights to the right people sooner. These systems help firms spot opportunities sooner, autho...
Pinterest is a popular Web application that has over 250 million active users. It is a visual discovery engine for finding ideas for recipes, fashion, weddings, home decoration, and much more. In the last year, the company decided to create a knowledge graph that aims to represent the vast amount...