Keynote

Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox [1]. 

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.

Big Data has made many data processing tasks significantly easier over the past couple of years. We now have the capability to perform data processing like never before. However, Big Data comes with a Big Assumption: that we can bring lots of data together in one homogeneous dataset. But what if we can’t?

At Capital One, we're defining and creating data relationships across divisions and systems, helping the enterprise to better understand data while enhancing analytical capabilities through standardization, improved context, and increased efficiency.