Many organizations have initiated data and analytics transformations with some success, however are beginning to face challenges in scaling efforts beyond a handful of applications/use cases. One of the major barriers remains around data management, including challenges with data transparency, integration, quality, and accessibility. Data products represent the next wave of progress for the enterprise – defining explicit shared meaning around data; designed with scale and value capture in mind. In the talk, we walk through the challenges faced today and propose an approach to develop and maintain data products - including the team, funding model and use of knowledge graphs.
How do falsehoods spread on the web? This and other questions related to the propagation of fake news and biased discourse in the public area have been drawing increasing interest in different communities from social sciences to artificial intelligence. Online discourse, i.e. claims and opinions ...
The concept of an Open Knowledge Network (OKN) is one of the components of the National Science Foundation’s Harnessing the Data Revolution (HDR) Big Idea, with the objective of providing semantic information infrastructure. By encoding information and knowledge about real-world entities and thei...
In this talk we describe a new technique for merging knowledge graphs: translating the knowledge graph schemas into categories and the knowledge graph data into functors, then applying the "co-limit/pushout" construction from a branch of mathematics called category theory to merge these categorie...
