Recently a number of enterprises have been employing semantic technologies to improve the means by which their content is structured, classified, made available to content consumers and its performance measured. A knowledge base this resultantly interlinked content is increasingly referred to as a content graph.
Given that the term “knowledge graph” is notoriously ill-defined, any attempt to describe a “content graph” must begin by addressing some fundamental questions. What is a content graph? How does it differ from a knowledge graph, or is such a distinction even valid? What, for that matter, is “content”?
In the course of answering these questions, Aaron will build a picture of a content graph’s capabilities and promise, and in particular its ability to power an intelligent content ecosystem. Drawing on his experiences at Electronic Arts, Aaron will show how a content graph is ideally suited to meet the requirements of generating well-described, endpoint-agnostic content, and how a content graph can be leveraged for both superior analytics insights and a scalable approach to personalization.
Aaron will conclude by looking at some of the challenges of content graphs and in particular the still-ambiguous role of content models in a such a graph, including how content model standards (or lack thereof) are related to those used by ontologies and taxonomies.
Knowledge Graph Strategist
Electronic Arts
https://www.ea.com
Aaron Bradley works as a Knowledge Graph Strategist at Electronic Arts in Vancouver, Canada. With a background in library science, web design and search engine marketing, Aaron now works as part of a team that is focused on the development, expansion and improvement of EA’s knowledge graph.
